Code Instructions Research Articles

PRONA: An R-package for Patient Reported Outcomes Network Analysis.

Network analysis (NA) has recently emerged as a new paradigm by which to model the symptom patterns of patients with complex illnesses such as cancer. NA uses graph theory-based methods to capture the interplay between symptoms and identify which symptoms may be most impactful to patient quality of life and are therefore most critical to treat/prevent. Despite NA's increasing popularity in research settings, its clinical applicability is hindered by the lack of a unified platform that consolidates all the software tools needed to perform NA, and by the lack of methods for capturing heterogeneity across patient cohorts. Addressing these limitations, we present PRONA, an R-package for Patient Reported Outcomes Network Analysis. PRONA not only consolidates previous NA tools into a unified, easy-to-use analysis pipeline, but also augments the traditional approach with functionality for performing unsupervised discovery of patient subgroups with distinct symptom patterns. PRONA is implemented in R. Source code, installation, and use instructions are available on GitHub at https://github.com/bbergsneider/PRONA. Supplementary information is available at Bioinformatics online.

Expressive 3D Facial Animation Generation Based on Local-to-Global Latent Diffusion.

3D Facial animations, crucial to augmented and mixed reality digital media, have evolved from mere aesthetic elements to potent storytelling media. Despite considerable progress in facial animation of neutral emotions, existing methods still struggle to capture the authenticity of emotions. This paper introduces a novel approach to capture fine facial expressions and generate facial animations using audio synchronization. Our method consists of two key components: First, the Local-to-global Latent Diffusion Model (LG-LDM) tailored for authentic facial expressions, which can integrate audio, time step, facial expressions, and other conditions towards possible encoding of emotionally rich yet latent features in response to possibly noisy raw audio signals. The core of LG-LDM is our carefully designed Facial Denoiser Model (FDM) for aligning the local-to-global animation feature with audio. Second, we redesign an Emotion-centric Vector Quantized-Variational AutoEncoder framework (EVQ-VAE) to finely decode the subtle differences under different emotions and reconstruct the final 3D facial geometry. Our work significantly contributes to the key challenges of emotionally realistic 3D facial animation for audio synchronization and enhances the immersive experience and emotional depth in augmented and mixed reality applications. We provide a reproducibility kit including our code, dataset, and detailed instructions for running the experiments. This kit is available at https://github.com/wangxuanx/Face-Diffusion-Model.

SYSTEMATIC AND LEGAL ASPECTS OF LEGAL RESPONSIBILITY FOR ILLEGAL ACTS WITH AMBER

The article is devoted to the disclosure of certain systemic and legal aspects of responsibility for illegal actions with amber. Relevant judicial practice is analyzed. Proposals for improvement of Art. 240-1 of the Criminal Code of Ukraine “Illegal extraction, sale, purchase, transfer, forwarding, transportation, processing of amber”. The issue of the application of the criminal law norm of insignificance for the legal assessment of illegal amber mining and its illegal handling was considered. The existence of a conflict between the investigated criminal law ban and Art. 47 of the Code of Ukraine on Administrative Offenses “Violation of the State Property Right to Subsoil”, comments are expressed regarding its (collision) overcoming in the aspects of de lege lata and de lege ferenda. It is argued that the socially dangerous consequences of illegal mineral extraction (in particular, amber) should be labeled as damages. It is shown that carried out within the limits of Art. 240-1 of the Criminal Code of Ukraine, the differentiation of responsibility related to the size of illegal actions with amber is insufficient. The relevant provisions of the project of the new Criminal Code of Ukraine were analyzed, the advantages and disadvantages of the relevant formulations were clarified. In particular, the proposal of the drafters of a potential criminal law to distinguish the grounds of criminal responsibility for the illegal acquisition of natural resources, on the one hand, and individual manifestations of involvement in the behavior of those who commit illegal extraction of natural resources from their natural environment, on the other, was supported. The issue of the appropriateness of anchoring in Part 1 of Art. 240-1 of the Criminal Code of Ukraine instructions on actions other than illegal amber mining.

Adapting OGC's SensorThings API and data model to support data management and sharing for environmental sensors

Software is critical in managing environmental sensor data. The Open Geospatial Consortium (OGC) developed the “OGC SensorThings API” (STA) standard to address variability across sensors, observed variables, platforms, and protocols, facilitating development of sensing and Internet of Things applications. This paper details a Python/Django implementation of the STA application programming interface (API) and a PostgreSQL/Timescale implementation of the STA data model, enhancing availability of robust software for management and sharing of environmental sensor data. STA offers a RESTful interface with JSON data encoding, aligning with modern development patterns and facilitating interoperability. Integration of metadata from the Observations Data Model ensures data can be adequately described and interpreted. STA's flexibility allows lightweight query responses or comprehensive metadata inclusion, and a complementary data management API enhances use of STA within multi-user systems. Open-source code and deployment instructions in GitHub enable standalone or cloud deployments, enhancing accessibility and usability for researchers and practitioners.

Procedurally generated AI compound media for expanding audial creations, broadening immersion and perception experience

Recently, the world has been gaining vastly increasing access to more and more advanced artificial intelligence tools. This phenomenon does not bypass the world of sound and visual art, and both of these worlds can benefit in ways yet unexplored, drawing them closer to one another. Recent breakthroughs open possibilities to utilize AI driven tools for creating generative art and using it as a compound of other multimedia. The aim of this paper is to present an original concept of using AI to create a visual compound material to existing audio source. This is a way of broadening accessibility thus appealing to different human senses using source media, expanding its initial form. This research utilizes a novel method of enhancing fundamental material consisting of text audio or text source (script) and sound layer (audio play) by adding an extra layer of multimedia experience – a visual one, generated procedurally. A set of images generated by AI tools, creating a story-telling animation as a new way to immerse into the experience of sound perception and focus on the initial audial material. The main idea of the paper consists of creating a pipeline, form of a blueprint for the process of procedural image generation based on the source context (audial or textual) transformed into text prompts and providing tools to automate it by programming a set of code instructions. This process allows creation of coherent and cohesive (to a certain extent) visual cues accompanying audial experience levering it to multimodal piece of art. Using nowadays technologies, creators can enhance audial forms procedurally, providing them with visual context. The paper refers to current possibilities, use cases, limitations and biases giving presented tools and solutions.

A Hardware Security Protection Method for Conditional Branches of Embedded Systems.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Mutual annotation-based prediction of protein domain functions with Domain2GO.

Identifying unknown functional properties of proteins is essential for understanding their roles in both health and disease states. The domain composition of a protein can reveal critical information in this context, as domains are structural and functional units that dictate how the protein should act at the molecular level. The expensive and time-consuming nature of wet-lab experimental approaches prompted researchers to develop computational strategies for predicting the functions of proteins. In this study, we proposed a new method called Domain2GO that infers associations between protein domains and function-defining gene ontology (GO) terms, thus redefining the problem as domain function prediction. Domain2GO uses documented protein-level GO annotations together with proteins' domain annotations. Co-annotation patterns of domains and GO terms in the same proteins are examined using statistical resampling to obtain reliable associations. As a use-case study, we evaluated the biological relevance of examples selected from the Domain2GO-generated domain-GO term mappings via literature review. Then, we applied Domain2GO to predict unknown protein functions by propagating domain-associated GO terms to proteins annotated with these domains. For function prediction performance evaluation and comparison against other methods, we employed Critical Assessment of Function Annotation 3 (CAFA3) challenge datasets. The results demonstrated the high potential of Domain2GO, particularly for predicting molecular function and biological process terms, along with advantages such as producing interpretable results and having an exceptionally low computational cost. The approach presented here can be extended to other ontologies and biological entities to investigate unknown relationships in complex and large-scale biological data. The source code, datasets, results, and user instructions for Domain2GO are available at https://github.com/HUBioDataLab/Domain2GO. Additionally, we offer a user-friendly online tool at https://huggingface.co/spaces/HUBioDataLab/Domain2GO, which simplifies the prediction of functions of previously unannotated proteins solely using amino acid sequences.

Playing with the child within: Early parenthood experiences of parents who were out-of-home placed during childhood

Experience of out-of-home care (OHC) is associated with life-long vulnerability, and research indicates that parents with experiences of OHC are at higher risk of having their own children placed in out-of-home care. However, research on these parents is limited and lacks a resilience perspective. Here, we focused on parents with OHC experience as children but with custody of their own children (N = 12) and asked where these parents can draw strength in parenting, using a standardized interview with focus on thoughts, feelings, and parenting experiences. Importantly, all content related to OHC experience was disclosed in the context of discussing parental caregiving, without any specific questions asked. We investigated whether caregiving representations associated with the ability to promote security in the child were linked to parents’ attachment scripts, comprising implicit expectations of sensitivity and helpfulness in attachment-related interactions, or reflective functioning, denoting the capacity to take the mental perspective of the child. We coded interviews following standardized coding instructions, for reflective thinking and other important variables that capture strategies for processing relational and emotional themes. Narratives in which the parents had spontaneously made references to their childhood experiences were coded through qualitative content analysis. Results showed that autonomous caregiving representations were associated with the content in parents’ attachment scripts but not their reflective functioning. Themes reveal distinct tendencies in these parents’ caregiving representations: Own adverse childhood experiences were often expressed as separation anxiety or feelings of guilt in relation to parenting. An unmet need for support was common , as were concerns about stigma and vulnerability associated with disseminating information about their background. Nevertheless, parental caregiving involves for those parents motivation to break patterns of adversity, provide sensitive parenting, but also understand, accept oneself and develop.

Abstract TP98: Data Quality Review: Opportunities Through Transparency

Background: Manual abstraction is still the leading source of registry data entry. Due to variability, information should be reviewed for accuracy. Clinical processes that impact quality of care and hospital metrics are abstracted and entered in the Get with The Guidelines® (GWTG) suite of registry programs. GWTG data is intended to be abstracted in accordance with specified coding instructions with high integrity. Created in 2023, the Data Quality Review Program (DQR) reviews the medical record concurrently with abstracted GWTG data to help streamline processes and ensure accuracy of data entry. The information is then compiled to form an inner-rater reliability (IRR) report including performance improvement opportunities. Methods: Site participation is voluntary and a percentage of annual charts is selected. Records in GWTG are compared to corresponding patient records via screen sharing for accuracy. Discrepancies and opportunities are reviewed with each case including clinical pathway performance, regulatory standards, and coding elements. Chart review includes data used for GWTG achievement and quality awards, time-sensitive activities and social disparities of health. IRR is further supported by individual risk and occurrence factors for each element reviewed along with a total composite IRR score guided by a standardized framework. Results: From April to July 2023, 50 hospitals have completed the DQR. Sites have varied geographically, in intervention capabilities, and accrediting body. Sites completed an experience survey after the DQR using Likert scale and narrative questions. 100% of survey respondents rated the DQR a 5 the top score, and valued evidence-based education and discussion of site abstraction processes. DQR reports were presented during regulatory body surveys in 6 cases which resulted in “no findings” note in this component for the site. Conclusion: While abstraction is not directly related to clinical care there are opportunities to connect registry data entry to improvement, both in quality and delivery of care. Sites participating in the DQR feel better prepared for certification, validation of current practices, and enhanced knowledge of coding instructions demonstrated through quality matrix recommendations.

RefseqR: an R package for common computational operations with records on RefSeq collection

Abstract Summary We introduce refseqR, an R package that offers a user-friendly solution, enabling common computational operations on RefSeq entries (GenBank, NCBI). The package is specifically designed to interact with records curated from the RefSeq database. Most importantly, the interoperability and integration with several Bioconductor objects allow connections to be applied to other projects. Availability and implementation The package refseqR is implemented in R and published under the MIT open-source license. The source code, documentation, and usage instructions are available on CRAN (https://CRAN.R-project.org/package=refseqR).

Nf-core/nanostring: a pipeline for reproducible NanoString nCounter analysis.

The NanoString™ nCounter® technology platform is a widely used targeted quantification platform for the analysis of gene expression of up to ∼800 genes. Whereas the software tools by the manufacturer can perform the analysis in an interactive and GUI driven approach, there is no portable and user-friendly workflow available that can be used to perform reproducible analysis of multiple samples simultaneously in a scalable fashion on different computing infrastructures. Here, we present the nf-core/nanostring open-source pipeline to perform a comprehensive analysis including quality control and additional features such as expression visualization, annotation with additional metadata and input creation for differential gene expression analysis. The workflow features an easy installation, comprehensive documentation, open-source code with the possibility for further extensions, a strong portability across multiple computing environments and detailed quality metrics reporting covering all parts of the pipeline. nf-core/nanostring has been implemented in the Nextflow workflow language and supports Docker, Singularity, Podman container technologies as well as Conda environments, enabling easy deployment on any Nextflow supported compatible system, including most widely used cloud computing environments such as Google GCP or Amazon AWS. The source code, documentation and installation instructions as well as results for continuous tests are freely available at https://github.com/nf-core/nanostring and https://nf-co.re/nanostring.

MHCSeqNet2-improved peptide-class I MHC binding prediction for alleles with low data.

The binding of a peptide antigen to a Class I major histocompatibility complex (MHC) protein is part of a key process that lets the immune system recognize an infected cell or a cancer cell. This mechanism enabled the development of peptide-based vaccines that can activate the patient's immune response to treat cancers. Hence, the ability of accurately predict peptide-MHC binding is an essential component for prioritizing the best peptides for each patient. However, peptide-MHC binding experimental data for many MHC alleles are still lacking, which limited the accuracy of existing prediction models. In this study, we presented an improved version of MHCSeqNet that utilized sub-word-level peptide features, a 3D structure embedding for MHC alleles, and an expanded training dataset to achieve better generalizability on MHC alleles with small amounts of data. Visualization of MHC allele embeddings confirms that the model was able to group alleles with similar binding specificity, including those with no peptide ligand in the training dataset. Furthermore, an external evaluation suggests that MHCSeqNet2 can improve the prioritization of T cell epitopes for MHC alleles with small amount of training data. The source code and installation instruction for MHCSeqNet2 are available at https://github.com/cmb-chula/MHCSeqNet2.

LEGAL ANALYSIS OF THE PRACTICE OF TRANSFER OF RIGHTS BY FINANCING INSTITUTIONS THROUGH AN ABSOLUTE POWER OF POWER: A REVIEW OF VIOLATIONS OF LAW AND ITS LEGAL IMPACT

This research aims to examine unlawful practices committed by financing institutions in the process of transferring rights by using absolute power of attorney and examine the legal consequences of using absolute power of attorney against the law. The research uses a statutory approach and applicable regulations along with legal principles used to examine the problem under study. The conclusion obtained is that in practice there are still many financing institutions in collaboration with authorized officials who still practice debt and credit mixed with buying and selling, therefore the use of absolute power of attorney in the transfer process is a legal act prohibited by laws and regulations which can be canceled and null and void and is a form of abuse of circumstances and vigilante efforts. The legal consequences of the practice of unilateral transfer of rights using an absolute power of attorney are a form of legal smuggling where when the conditions for the validity of the agreement are not met, which include subjective conditions in the form of an agreement containing elements of unlawful acts and objective conditions regarding legal causes because they violate the Civil Code and Presidential Instruction Number 14 of 1982 concerning the Prohibition of the Use of Absolute Power of Attorney as a Transfer of Land Rights which was later replaced and refined in Government Regulation No. 24 of 1997 concerning Land Registration.

Shake table testing of a half-scale stone masonry building aggregate

Masonry aggregates have developed throughout city centres of Europe due to a centuries-long densification process that generally lacked consistent planning or engineering. Adjacent units are connected either through interlocking stones or a layer of mortar. Without interlocking stones, the connection between the units is weak, and an out of-phase response of the units can lead to separation and pounding. Modelling guidelines and code instructions are missing for modelling the interaction of such adjacent units because of scarce experimental data. Therefore, in this study an unreinforced stone masonry aggregate was tested on the bidirectional shake table with an incremental seismic protocol as a part of the SERA AIMS—Adjacent Interacting Masonry Structures project. The aggregate was constructed at half-scale with double-leaf undressed stone masonry without interlocking between the units. Floors were built with timber beams and one layer of planks, with different beam span orientation for each unit. After significant damage, one of the units was retrofitted by anchoring the timber beams to the walls to prevent out-of-plane failure and testing was continued. Significant interaction between the units was observed with specific damage mechanisms. Cracking and separation were observed at the interface in both longitudinal and transverse direction, starting at lower intensity runs and progressively increasing. Bidirectional seismic excitation affected the unit separation, with friction forces seemingly playing a role in the transverse direction. Signs of pounding at the interface were observed during higher intensity runs, together with the formation of a soft storey mechanism at the upper storey of the higher unit. The mechanism involved an out-of-plane response of the shared wall, with a horizontal crack at the height of the interaction. These findings contribute to a better understanding of the seismic behaviour of masonry aggregates.

APEAR: an R package for autonomous visualization of pathway enrichment networks.

The interpretation of pathway enrichment analysis results is frequently complicated by an overwhelming and redundant list of significantly affected pathways. Here, we present an R package aPEAR (Advanced Pathway Enrichment Analysis Representation) which leverages similarities between the pathway gene sets and represents them as a network of interconnected clusters. Each cluster is assigned a meaningful name that highlights the main biological themes in the experiment. Our approach enables an automated and objective overview of the data without manual and time-consuming parameter tweaking. The package aPEAR is implemented in R, published under the MIT open-source licence. The source code, documentation, and usage instructions are available on https://gitlab.com/vugene/aPEAR as well as on CRAN (https://CRAN.R-project.org/package=aPEAR).

Img2fmri: a python package for predicting group-level fMRI responses to visual stimuli using deep neural networks.

Here we introduce a new python package, img2fmri, to predict group-level fMRI responses to individual images. This prediction model uses an artificial deep neural network (DNN), as DNNs have been successful at predicting cortical responses in the human visual cortex when trained on real world visual categorization tasks. To validate our model, we predict fMRI responses to images our model has not previously seen from a new dataset. We then show how our frame-by-frame prediction model can be extended to a continuous visual stimulus by predicting an fMRI response to Pixar Animation Studio's short film Partly Cloudy. In analyzing the timepoint-timepoint similarity of our predicted fMRI response around human-annotated event boundaries in the movie, we find that our model outperforms the baseline model in describing the dynamics of the real fMRI response around these event boundaries, particularly in the timepoints just before and at an event. These analyses suggest that in visual areas of the brain, at least some of the temporal dynamics we see in the brain's processing of continuous, naturalistic stimuli can be explained by dynamics in the stimulus itself, since they can be predicted from our frame-by-frame model. All code, analyses, tutorials, and installation instructions can be found at https://github.com/dpmlab/img2fmri.

This Shall Be Your Wisdom (Deut. 4:6): The Wisdom of Deuteronomy Amidst Changing Paradigms for Hebrew Wisdom

Abstract The death of ‘Wisdom Literature’ as a literary genre that has controlled understandings of wisdom in the Hebrew Bible has resulted in a need for new investigations of the concept of wisdom in biblical books outside the ‘Wisdom Literature’, which have often been considered to be ‘influenced’ by the artificial genre of ‘Wisdom Literature’. This paper will analyse the relationship between wisdom and law in the book of Deuteronomy, in order to show how freeing the concept of wisdom in Deuteronomy from the influence of ‘Wisdom Literature’ opens up perspectives to appreciate the wisdom of Deuteronomy on its own terms as a unique contribution to conceptions of Hebrew wisdom. Rather than thinking of the wisdom of Deuteronomy as being influenced by ‘Wisdom Literature’, the wisdom terminology in Deuteronomy is better accounted for by Deuteronomy’s rhetorical appropriation of the ancient Near Eastern Instruction and Law Code literary forms, each of which focuses on transmitting wisdom for successful living. The wisdom of Deuteronomy, which is cultivated throughout the book in the literary forms of Instruction and Law Code for the transmission of knowledge and intellectual skill in Torah interpretation and application, is distinct yet complementary to the wisdom found in the traditional ‘Wisdom’ books, Proverbs, Job, and Ecclesiastes.

Effective Memory Diversification in Legacy Systems

Memory corruption error is one of the critical security attack vectors against a wide range of software. Addressing this problem, modern compilers provide multiple features to fortify the software against such errors. However, applying compiler-based memory defense is problematic in legacy systems we often encounter in industry or military environments because source codes are unavailable. In this study, we propose memory diversification techniques tailored for legacy binaries to which we cannot apply state-of- the-art compiler-based solutions. The basic idea of our approach is to automatically patch the machine code instructions of each legacy system differently (e.g., a drone, or a vehicle firmware) without altering any semantic behavior of the software logic. As a result of our system, attackers must create a specific attack payload for each target by analyzing the particular firmware, thus significantly increasing exploit development time and cost. Our approach is evaluated by applying it to a stack and heap of multiple binaries, including PX4 drone firmware and other Linux utilities.

Improvements on Hiding x86-64 Instructions by Interleaving

This paper presents the results of a new method for interleaving CPU instructions in x86-64 machine code, such that one can hide executable code within other valid instructions. The aim is to make it more difficult to reverse-engineer software at a machine code level – to obfuscate instructions. A result is a hidden execution path within a visible main instruction path. While previous methods for this instruction obfuscation exist, we present a new method which builds upon past work, and which allows a greater flexibility in the selection of main instruction path instructions. The result of this new approach is to provide a methodology for instruction concealment which is free of restrictions present in prior work.

HiMAP2: Identifying phylogenetically informative genetic markers from diverse genomic resources.

Multiplexed amplicon sequencing offers a cost-effective and rapid solution for phylogenomic studies that include a large number of individuals. Selecting informative genetic markers is a critical initial step in designing such multiplexed amplicon panels, but screening various genomic data and selecting markers that are informative for the question at hand can be laborious. Here, we present a flexible and user-friendly tool, HiMAP2, for identifying, visualizing and filtering phylogenetically informative loci from diverse genomic and transcriptomic resources. This bioinformatics pipeline includes orthology prediction, exon extraction and filtering of aligned exon sequences according to user-defined specifications. Additionally, HiMAP2 facilitates exploration of the final filtered exons by incorporating phylogenetic inference of individual exon trees with raxml-ng as well as the estimation of a species tree using astral. Finally, results of the marker selection can be visualized and refined with an interactive Bokeh application that can be used to generate publication-quality figures. Source code and user instructions for HiMAP2 are available at https://github.com/popphylotools/HiMAP_v2.