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PubMed1998

Zero-crossing derivative spectrophotometry for the determination of haloperidol in presence of parabens.

First derivative spectrophotometry with a zero-crossing technique of measurement is used for the quantitative determination of haloperidol in the presence of methylparaben and propylparaben, which is added as antimicrobial preservatives in pharmaceuticals. This technique permits the quantification of haloperidol in the presence of parabens, with closely overlapping spectral bands, and without any separation step. Linear calibration graphs of first derivative values (at 255.2 nm for haloperidol) versus concentration (in the range 4.0-20.0 micrograms ml-1) were obtained with negligible intercepts. Relative standard deviation of 0.83% was obtained for intra-day precision and 1.86% for inter-day precision. The recovery of haloperidol in synthetic mixtures with parabens and pharmaceutical dosage forms is also reported.

Calibration↗

PubMed2025

Autocycler: long-read consensus assembly for bacterial genomes.

MOTIVATION: Long-read sequencing enables complete bacterial genome assemblies, but individual assemblers are imperfect and often produce sequence-level and structural errors. Consensus assembly using Trycycler can improve accuracy, but its lack of automation limits scalability. There is a need for an automated method to generate high-quality consensus bacterial genome assemblies from long-read data. RESULTS: We present Autocycler, a command-line tool for generating accurate bacterial genome assemblies by combining multiple alternative long-read assemblies of the same genome. Without requiring user input, Autocycler builds a compacted De Bruijn graph from the input assemblies, clusters and filters contigs, trims overlaps, and resolves consensus sequences by selecting the most common variant at each locus. It also supports manual curation when desired, allowing users to refine assemblies in challenging or important cases. In our evaluation using Oxford Nanopore Technologies reads from five bacterial isolates, Autocycler outperformed individual assemblers, automated pipelines, and other consensus tools, producing assemblies with lower error rates and improved structural accuracy. AVAILABILITY AND IMPLEMENTATION: Autocycler is implemented in Rust, open-source, and freely available at github.com/rrwick/Autocycler. It runs on Linux and macOS and is extensively documented.

Genome, Bacterial↗

PubMed1999

Automatic karyotyping of metaphase cells with overlapping chromosomes.

Image analysis research aimed at analyzing images of banded, human chromosomes is described. An experimental automated karyotyping system (AKS) has been assembled. The goal of the system is to label the chromosomes from a metaphase image with minimal human intervention. The AKS is unique in that it is designed to automatically process cells containing overlapping chromosomes. It also has four novel algorithmic features: the use of cross section sequence graphs, an over-segmentation-based segmentation strategy, the use of the 'pale path' concept for banded chromosomes and the application of mathematical programming for cell-level chromosome classification in cells with overlapping chromosomes. System performance was analyzed using a variety of metaphase images exhibiting various levels of overlapping chromosomes.

Algorithms↗

PubMed1997

DNA binding characteristics of CrtJ. A redox-responding repressor of bacteriochlorophyll, carotenoid, and light harvesting-II gene expression in Rhodobacter capsulatus.

Previous genetic analysis indicated that the photosynthesis gene cluster from Rhodobacter capsulatus coded for the transcription factor, CrtJ, that is responsible for aerobic repression of bacteriochlorophyll, carotenoid, and light harvesting-II gene expression. In this study, we have heterologously overexpressed and purified CrtJ to homogeneity and shown by gel mobility shift assays that CrtJ is biologically active. DNase I footprint analysis confirms molecular genetic studies by showing that CrtJ binds to conserved palindromic sequences that overlap the -10 and -35 promoter regions of the bchC operon. Graphs of the percentage of DNA bound versus protein concentration show sigmoidal curves, which is highly indicative of cooperative binding of CrtJ to the two palindromic sites. A binding constant for interaction of CrtJ with the palindrome that spans the -10 region was calculated to be 4.8 x 10(-9) M, whereas affinity for the palindrome that spans the -35 region was found to be 2.9 x 10(-9) M. Binding of CrtJ to the bchC promoter region was also found to be redox-sensitive, with CrtJ exhibiting a 4.5-fold higher binding affinity under oxidizing versus reducing conditions.

Bacterial Proteins↗

PubMed2001

Use of ground-penetrating radar to study tree roots in the southeastern United States.

The objectives of our study were to assess the feasibility of using ground-penetrating radar (GPR) to study roots over a broad range of soil conditions in the southeastern United States. Study sites were located in the Southern Piedmont, Carolina Sandhills and Atlantic Coast Flatwoods. At each site, we tested for selection of the appropriate antenna (400 MHz versus 1.5 GHz), determined the ability of GPR to resolve roots and buried organic debris, assessed root size, estimated root biomass, and gauged the practicality of using GPR. Resolution of roots was best in sandy, excessively drained soils, whereas soils with high soil water and clay contents seriously degraded resolution and observation depth. In the Carolina Sandhills, 16 1 x 1-m plots were scanned with the 1.5 GHz antenna using overlapping grids. Plots were subsequently excavated, larger roots (> 0.5 cm diameter) sketched on graph paper before removal, and all roots oven-dried, classified by size and weighed. Roots as small as 0.5 cm in diameter were detected with GPR. We were able to size roots (0.5 to 6.5 cm in diameter) that were oriented perpendicular to the radar sweep (r(2) = 0.81, P = 0.0004). Use of image analysis software to relate the magnitude of radar parabolas to actual root biomass resulted in significant correlations (r(2) = 0.55, P = 0.0274). Orientation and geometry of the reflective surface seemed to have a greater influence on parabola dimensions than did root size. We conclude that the utility of current GPR technology for estimating root biomass is site-specific, and that GPR is ineffective in soils with high clay or water content and at sites with rough terrain (most forests). Under particular soil and site conditions, GPR appears to be useful for augmenting traditional biomass sampling.

Biomass↗

PubMed2003

Layout search of a gene regulatory network for 3-D visualization.

In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large effective graph. This paper proposes a method which allows for coping with an increase in the number of nodes by laying out genes on planes of several layers and then overlapping these planes. This layout involves an optimization problem which requires maximizing the fitness function. To demonstrate the effectiveness of our approach, we show some graphs using actual data on 82 genes and 552 genes. We also describe how to lay out nodes by means of stochastic searches, e.g., stochastic hill-climbing and incremental methods. The experimental results show the superiority and usefulness of two search methods in comparison with the simple random search.

Gene Expression Regulation↗

PubMed2003

Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures.

This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin-Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures.

Algorithms↗

PubMed1988

Second-derivative spectrophotometric assay of mixtures of dicloxacillin sodium and ampicillin sodium in pharmaceuticals.

'Zero-crossing' derivative spectrophotometry has been used for determining binary mixtures of dicloxacillin Na and ampicillin Na, which are penicillins with closely overlapping absorption spectra. The procedure is rapid, simple, nondestructive, and does not require solutions of equations. Calibration graphs are linear (r = 0.9999), with a zero intercept for up to 60 micrograms/mL of each antibiotic. Detection limits at the p = 0.05 level of significance were calculated to be 0.29 and 0.31 microgram/mL of dicloxacillin Na and ampicillin Na, respectively. The method was successfully applied to the assay of commercial injections and capsules for these drugs.

Ampicillin↗

PubMed1996

Good maps are straight.

This paper proposes a simplified approach to the assembly of large physical genome maps. The approach focuses on two key problems: (i) the integration of diverse forms of data from numerous sources, and (ii) the detection and removal of errors and anomalies in the data. The approach simplifies map assembly by dividing it into three phases-overlap, linkage and ordering. In the first phase, all forms of overlap data are integrated into a simple abstract structure, called clusters, where each cluster is a set of mutually-overlapping DNA segments. This phase filters out many questionable overlaps in the mapping data. In the second phase, clusters are linked together into a weighted intersection graph. False links between widely separated regions of the genome show up as crooked, branching structures in the graph. Removing these false links produces graphs that are straight, reflecting the linear structure of chromosomes. From these straight graphs, the third phase constructs a physical map. Graph algorithms and graph visualization play key roles in implementing the approach. At present, the approach is at an early stage of development: it has been tested on real and simulated mapping data, and the results look promising. This paper describes the first two phases of the approach in detail, and reports on our progress to date.

Chromosomes, Human, Pair 7↗

PubMed1995

Computer-assisted assignment of peptides with non-standard amino acids.

A comprehensive peptide assignment program and its application to a cyclic peptide, cyclosporin A, are presented in this paper. A group of graph theoretical algorithms using fuzzy logic are discussed with the aid of examples from cyclosporin A. The algorithms deal with heavily overlapped peaks, recover disjointed and distorted spin coupling networks, and include strategies for sequence-specific assignment. A procedure to extend the Protein Knowledge Base for automatically assigning non-standard amino acid residues is also presented. The program is capable of completely automated assignment for small peptides (approximately 20 residues). For such molecules, it is insensitive to whether the peptide chain is cyclic or acyclic, and to whether amide protons are present or absent. For larger peptides/proteins, more user interaction is required and the sequence-specific assignment step usually must proceed through fragments smaller than the full length to avoid problems due to occurrence of a combinatorial explosion. The program can be applied as a rigorous tool to check manual assignments. The fuzzy graph theoretical concepts built in the program are illustrated with 2D proton spectra of a peptide, but may be extended to higher-dimensional spectra, other biopolymers, natural products and other organic structures.

Algorithms↗

PubMed1997

Three-dimensional modeling of renal glomerular capillary networks.

OBJECTIVE: To quantitatively characterize three-dimensional (3D) vascular patterns based on the path length distribution in networks. STUDY DESIGN: Volume data were stacks of confocal fluorescence images of renal glomeruli, obtained using a confocal laser scanning microscope and spanning 60-130 microns in the z axis. After manual editing to remove nonglomerular components, transverse sections of glomerular capillary lumens were segmented automatically using two-dimensional morphologic filters. The center points and the overlap (between adjacent sections) of lumens segmented in the x vs. y plane were used to derive a graph (i.e., a multiply connected network) for each glomerulus. The average degree and the distance between connected nodes were used to derive the number of graph edges and the overall length of the capillary network. RESULTS: Renderings of the 3D reconstructions demonstrated well the lobular structure of the glomerular tufts. Mean capillary length ranged from 53 to 180 microns in 10 glomeruli. Total capillary length ranged from 3,500 to 9,500 microns (mean 5,833). CONCLUSION: Structural measurements based on confocal data require less effort than do measurements based on serial sections and make detailed study of diseased glomerular populations practical.

Animals↗

PubMed2026

plinkQC: an integrated tool for ancestry inference, sample selection, and quality control in population genetics.

MOTIVATION: Population genetic analyses rely on high quality datasets that pass rigorous controls for sample and marker quality. Many analyses also require additional processing including identification of ancestry and sample relatedness. A software package that addresses all these common, yet crucial tasks is missing. RESULTS: We have developed plinkQC, an R/CRAN package that combines these functionalities into a single software package with detailed vignettes for example applications. plinkQC determines the ancestry of study samples via a pre-trained random forest classifier that reaches 98% performance accuracy with just 5% of marker overlap between reference and user data. To obtain the maximal set of unrelated study samples, we developed a graph-based pruning method, taking both relationship estimates and sample quality into account. We demonstrate optimal sample selection on the 1000 Genomes project, where we retain an additional 71 samples compared to publicly available exclusion lists. Finally, plinkQC bundles these results together with per-individual and per-marker quality control checks into three simple functions and returns both the quality controlled dataset and quality control report about each step of the analysis. AVAILABILITY AND IMPLEMENTATION: plinkQC is available as an R/CRAN package. The documentation and code are available on github: https://meyer-lab-cshl.github.io/plinkQC/ and https://github.com/meyer-lab-cshl/plinkQC_manuscript.

Software↗

PubMed2004

BACUS: A Bayesian protocol for the identification of protein NOESY spectra via unassigned spin systems.

NMR frequency assignments are usually considered a prerequisite for the analysis of NOESY spectra, in turn required for the calculation of biomolecular structures. In contrast, as we propose here, relatively high numbers of unambiguous NOE identities can be consistently achieved in an automated manner by relying only on grouping resonances into connected spin systems. To achieve this goal, we have developed for proteins two protocols, SPI and BACUS, based on Bayesian inference. SPI (Grishaev and Llinás, 2002c) produces a list of the (1)H resonance frequencies from homo- and hetero-nuclear multidimensional spectra, grouped into effective spin systems. BACUS automatically establishes probabilistic identities of NOESY cross-peaks in terms of the chemical shifts provided by SPI. BACUS requires neither assignment of resonances nor an initial structural model. It successfully copes with chemical shift overlap and does so without cycling through 3D structure calculations. The method exploits the self-consistency of the NOESY graph by taking advantage of a network of J- as well as NOE-connected "reporter" protons sorted via SPI. BACUS was validated by tests on experimental NOESY data recorded for the col 2 and kringle 2 domains.

Bayes Theorem↗

PubMed1995

Four strikes against physical mapping of DNA.

Physical mapping is a central problem in molecular biology and the human genome project. The problem is to reconstruct the relative position of fragments of DNA along the genome from information on their pairwise overlaps. We show that four simplified models of the problem lead to NP-complete decision problems: Colored unit interval graph completion, the maximum interval (or unit interval) subgraph, the pathwidth of a bipartite graph, and the k-consecutive ones problem for k > or = 2. These models have been chosen to reflect various features typical in biological data, including false-negative and positive errors, small width of the map, and chimericism.

Base Sequence↗

PubMed2006

On the maximal cliques in c-max-tolerance graphs and their application in clustering molecular sequences.

Given a set S of n locally aligned sequences, it is a needed prerequisite to partition it into groups of very similar sequences to facilitate subsequent computations, such as the generation of a phylogenetic tree. This article introduces a new method of clustering which partitions S into subsets such that the overlap of each pair of sequences within a subset is at least a given percentage c of the lengths of the two sequences. We show that this problem can be reduced to finding all maximal cliques in a special kind of max-tolerance graph which we call a c-max-tolerance graph. Previously we have shown that finding all maximal cliques in general max-tolerance graphs can be done efficiently in O(n3 + out). Here, using a new kind of sweep-line algorithm, we show that the restriction to c-max-tolerance graphs yields a better runtime of O(n2 log n + out). Furthermore, we present another algorithm which is much easier to implement, and though theoretically slower than the first one, is still running in polynomial time. We then experimentally analyze the number and structure of all maximal cliques in a c-max-tolerance graph, depending on the chosen c-value. We apply our simple algorithm to artificial and biological data and we show that this implementation is much faster than the well-known application Cliquer. By introducing a new heuristic that uses the set of all maximal cliques to partition S, we finally show that the computed partition gives a reasonable clustering for biological data sets.

Journal Article↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMedDate not supplied

LINGO, an efficient holographic text based method to calculate biophysical properties and intermolecular similarities.

SMILES strings are the most compact text based molecular representations. Implicitly they contain the information needed to compute all kinds of molecular structures and, thus, molecular properties derived from these structures. We show that this implicit information can be accessed directly at SMILES string level without the need to apply explicit time-consuming conversion of the SMILES strings into molecular graphs or 3D structures with subsequent 2D or 3D QSPR calculations. Our method is based on the fragmentation of SMILES strings into overlapping substrings of a defined size that we call LINGOs. The integral set of LINGOs derived from a given SMILES string, the LINGO profile, is a hologram of the SMILES representation of the molecule described. LINGO profiles provide input for QSPR models and the calculation of intermolecular similarities at very low computational cost. The octanol/water partition coefficient (LlogP) QSPR model achieved a correlation coefficient R2=0.93, a root-mean-square error RRMS=0.49 log units, a goodness of prediction correlation coefficient Q2=0.89 and a QRMS=0.61 log units. The intrinsic aqueous solubility (LlogS) QSPR model achieved correlation coefficient values of R2=0.91, Q2=0.82, and RRMS=0.60 and QRMS=0.89 log units. Integral Tanimoto coefficients computed from LINGO profiles provided sharp discrimination between random and bioisoster pairs extracted from Accelrys Bioster Database. Average similarities (LINGOsim) were 0.07 for the random pairs and 0.36 for the bioisosteric pairs.

Biophysical Phenomena↗

PubMed2005

Uncovering the overlapping community structure of complex networks in nature and society.

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins, industrial sectors and groups of people) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.

Community Networks↗