Force Field X (FFX) is an open-source software package for atomic resolution modeling of genetic variants and organic crystals that leverages advanced potential energy functions and experimental data. FFX currently consists of nine modular packages with novel algorithms that include global optimization via a many-body expansion, acid-base chemistry using polarizable constant-pH molecular dynamics, estimation of free energy differences, generalized Kirkwood implicit solvent models, and many more. Applications of FFX focus on the use and development of a crystal structure prediction pipeline, biomolecular structure refinement against experimental datasets, and estimation of the thermodynamic effects of genetic variants on both proteins and nucleic acids. The use of Parallel Java and OpenMM combines to offer shared memory, message passing, and graphics processing unit parallelization for high performance simulations. Overall, the FFX platform serves as a computational microscope to study systems ranging from organic crystals to solvated biomolecular systems.

The manuscript `Modeling a unit cell: crystallographic refinement procedure using the biomolecular MD simulation platform Amber\' presents a novel protein structure refinement method claimed to offer improvements over traditional techniques like Refmac5 and Phenix. Our re-evaluation suggests that while the new method provides improvements, traditional methods achieve comparable results with less computational effort.

OBJECTIVE: The aim of the present research was to assess the mesiodistal angulation of the maxillary anterior teeth utilizing Image J computer software, a Profile projector, and a Custom-made jig.
METHODS: A total of 34 subjects (17 males and 17 females) were chosen from a group of 18-30 years old with bilateral Angle Class I molars and canine relationships. One manual approach (Custom-made jig) and two digital methods (J computer software, a Profile projector) were used to record the mesiodistal angulation in incisal view. The individuals had alginate impressions made, and a facebow was used to capture the maxilla\'s spatial relationship with the cranium. The articulated cast with the help of mounting ring moved to the specially customized jig, then the angulations was measured in the incisal view after the casts were placed in a semi-adjustable articulator. Data were recorded and statistically analyzed.
RESULTS: The mesiodistal angulation in the incisal view via three methods between the 17 males and 17 females has statistically significant different. Although the mesiodistal angulation for maxillary lateral incisor and canine did not show any statistically significant difference, the maximum and minimum values obtained were always greater in males in comparison with the females. This indicates that the positions of six maxillary anterior teeth in the males resulted in the creation of upward sweep of incisal edges of central and lateral incisors which was also referred to as \"smiling line\" producing masculine surface anatomy more squared and vigorous while feminine surface anatomy being more rounded, soft, and pleasant. There was no statistically significant difference between the right and left sides, indicating bilateral arch symmetry and the symmetrical place of the right teeth compared with the left side\'s corresponding teeth.
CONCLUSIONS: On conclusion, according to the current study\'s findings, all three approaches can measure the mesiodistal angulations of maxillary anterior teeth in incisal view with clinically acceptable accuracy. The digital methods, which included using the Image J computer software and the profile projector, achieved more accurate results than the manual method.
CONCLUSIONS: The outcomes of this study\'s mesiodistal angulations can be used as a reference for placing teeth in both fully and partially edentulous conditions. This study contributes to a better understanding of the importance of achieving the ideal occlusion in the Indian population by placing the maxillary anterior teeth at the proper mesiodistal angulation. How to cite this article: Shadaksharappa SH, Lahiri B, Kamath AG, et al. Evaluation of Mesiodistal Angulation of Maxillary Anterior Teeth in Incisal View Using Manual and Digital Methods: An In Vivo Study. J Contemp Dent Pract 2024;25(4):320-325.

RNA-seq has brought forth significant discoveries regarding aberrations in RNA processing, implicating these RNA variants in a variety of diseases. Aberrant splicing and single nucleotide variants (SNVs) in RNA have been demonstrated to alter transcript stability, localization, and function. In particular, the upregulation of ADAR, an enzyme that mediates adenosine-to-inosine editing, has been previously linked to an increase in the invasiveness of lung adenocarcinoma cells and associated with splicing regulation. Despite the functional importance of studying splicing and SNVs, the use of short-read RNA-seq has limited the community\'s ability to interrogate both forms of RNA variation simultaneously.
We employ long-read sequencing technology to obtain full-length transcript sequences, elucidating cis-effects of variants on splicing changes at a single molecule level. We develop a computational workflow that augments FLAIR, a tool that calls isoform models expressed in long-read data, to integrate RNA variant calls with the associated isoforms that bear them. We generate nanopore data with high sequence accuracy from H1975 lung adenocarcinoma cells with and without knockdown of ADAR. We apply our workflow to identify key inosine isoform associations to help clarify the prominence of ADAR in tumorigenesis.
Ultimately, we find that a long-read approach provides valuable insight toward characterizing the relationship between RNA variants and splicing patterns.

BACKGROUND: Fungi play a key role in several important ecological functions, ranging from organic matter decomposition to symbiotic associations with plants. Moreover, fungi naturally inhabit the human body and can be beneficial when administered as probiotics. In mycology, the internal transcribed spacer (ITS) region was adopted as the universal marker for classifying fungi. Hence, an accurate and robust method for ITS classification is not only desired for the purpose of better diversity estimation, but it can also help us gain a deeper insight into the dynamics of environmental communities and ultimately comprehend whether the abundance of certain species correlate with health and disease. Although many methods have been proposed for taxonomic classification, to the best of our knowledge, none of them fully explore the taxonomic tree hierarchy when building their models. This in turn, leads to lower generalization power and higher risk of committing classification errors.
RESULTS: Here we introduce HiTaC, a robust hierarchical machine learning model for accurate ITS classification, which requires a small amount of data for training and can handle imbalanced datasets. HiTaC was thoroughly evaluated with the established TAXXI benchmark and could correctly classify fungal ITS sequences of varying lengths and a range of identity differences between the training and test data. HiTaC outperforms state-of-the-art methods when trained over noisy data, consistently achieving higher F1-score and sensitivity across different taxonomic ranks, improving sensitivity by 6.9 percentage points over top methods in the most noisy dataset available on TAXXI.
CONCLUSIONS: HiTaC is publicly available at the Python package index, BIOCONDA and Docker Hub. It is released under the new BSD license, allowing free use in academia and industry. Source code and documentation, which includes installation and usage instructions, are available at https://gitlab.com/dacs-hpi/hitac .

Recent advancements in genome assembly have greatly improved the prospects for comprehensive annotation of Transposable Elements (TEs). However, existing methods for TE annotation using genome assemblies suffer from limited accuracy and robustness, requiring extensive manual editing. In addition, the currently available gold-standard TE databases are not comprehensive, even for extensively studied species, highlighting the critical need for an automated TE detection method to supplement existing repositories. In this study, we introduce HiTE, a fast and accurate dynamic boundary adjustment approach designed to detect full-length TEs. The experimental results demonstrate that HiTE outperforms RepeatModeler2, the state-of-the-art tool, across various species. Furthermore, HiTE has identified numerous novel transposons with well-defined structures containing protein-coding domains, some of which are directly inserted within crucial genes, leading to direct alterations in gene expression. A Nextflow version of HiTE is also available, with enhanced parallelism, reproducibility, and portability.

Chromatin exhibits non-random distribution within the nucleus being arranged into discrete domains that are spatially organized throughout the nuclear space. Both the spatial distribution and structural rearrangement of chromatin domains in the nucleus depend on epigenetic modifications of DNA and/or histones and structural elements such as the nuclear envelope. These components collectively contribute to the organization and rearrangement of chromatin domains, thereby influencing genome architecture and functional regulation. This study develops an innovative, user-friendly, ImageJ-based plugin, called IsoConcentraChromJ, aimed quantitatively delineating the spatial distribution of chromatin regions in concentric patterns. The IsoConcentraChromJ can be applied to quantitative chromatin analysis in both two- and three-dimensional spaces. After DNA and histone staining with fluorescent probes, high-resolution images of nuclei have been obtained using advanced fluorescence microscopy approaches, including confocal and stimulated emission depletion (STED) microscopy. IsoConcentraChromJ workflow comprises the following sequential steps: nucleus segmentation, thresholding, masking, normalization, and trisection with specified ratios for either 2D or 3D acquisitions. The effectiveness of the IsoConcentraChromJ has been validated and demonstrated using experimental datasets consisting in nuclei images of pre-adipocytes and mature adipocytes, encompassing both 2D and 3D imaging. The outcomes allow to characterize the nuclear architecture by calculating the ratios between specific concentric nuclear areas/volumes of acetylated chromatin with respect to total acetylated chromatin and/or total DNA. The novel IsoConcentrapChromJ plugin could represent a valuable resource for researchers investigating the rearrangement of chromatin architecture driven by epigenetic mechanisms using nuclear images obtained by different fluorescence microscopy methods.

Since the acid growth theory was introduced in plant physiology and mainframe computers became more widely available in the mid-20th century, there has been a growing need to accurately predict plant cell morphological parameters during growth. This article presents a computer program that uses an original numerical method to solve a highly nonlinear growth equation. The program is written in Python, a popular open-source scientific software environment called CoCalc or SAGE. This program can be used to determine the growth of an individual plant cell or multicellular organ, such as a coleoptile or hypocotyl segment, at the non-meristemic limit. This standalone program is designed to be user-friendly and accessible to all readers, without barriers. With only a few key parameters, including pH and temperature, this program provides a practical set of tools for comparing growth-related experimental data across various areas of plant biology. Additionally, it could be useful in predicting plant growth during assisted migration, particularly in the face of climate change.

BACKGROUND: Shotgun metagenomics for microbial community survey recovers enormous amount of information for microbial genomes that include their abundances, taxonomic, and phylogenetic information, as well as their genomic makeup, the latter of which then helps retrieve their function based on annotated gene products, mRNA, protein, and metabolites. Within the context of a specific hypothesis, additional modalities are often included, to give host-microbiome interaction. For example, in human-associated microbiome projects, it has become increasingly common to include host immunology through flow cytometry. Whilst there are plenty of software approaches available, some that utilize marker-based and assembly-based approaches, for downstream statistical analyses, there is still a dearth of statistical tools that help consolidate all such information in a single platform. By virtue of stringent computational requirements, the statistical workflow is often passive with limited visual exploration.
RESULTS: In this study, we have developed a Java-based statistical framework ( https://github.com/KociOrges/cviewer ) to explore shotgun metagenomics data, which integrates seamlessly with conventional pipelines and offers exploratory as well as hypothesis-driven analyses. The end product is a highly interactive toolkit with a multiple document interface, which makes it easier for a person without specialized knowledge to perform analysis of multiomics datasets and unravel biologically relevant patterns. We have designed algorithms based on frequently used numerical ecology and machine learning principles, with value-driven from integrated omics tools which not only find correlations amongst different datasets but also provide discrimination based on case-control relationships.
CONCLUSIONS: CViewer was used to analyse two distinct metagenomic datasets with varying complexities. These include a dietary intervention study to understand Crohn\'s disease changes during a dietary treatment to include remission, as well as a gut microbiome profile for an obesity dataset comparing subjects who suffer from obesity of different aetiologies and against controls who were lean. Complete analyses of both studies in CViewer then provide very powerful mechanistic insights that corroborate with the published literature and demonstrate its full potential. Video Abstract.

Microbial pangenome analysis identifies present or absent genes in prokaryotic genomes. However, current tools are limited when analyzing species with higher sequence diversity or higher taxonomic orders such as genera or families. The Roary ILP Bacterial core Annotation Pipeline (RIBAP) uses an integer linear programming approach to refine gene clusters predicted by Roary for identifying core genes. RIBAP successfully handles the complexity and diversity of Chlamydia, Klebsiella, Brucella, and Enterococcus genomes, outperforming other established and recent pangenome tools for identifying all-encompassing core genes at the genus level. RIBAP is a freely available Nextflow pipeline at github.com/hoelzer-lab/ribap and zenodo.org/doi/10.5281/zenodo.10890871.