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PubMed2005

Conformational analysis and rotational barriers of alkyl- and phenyl-substituted urea derivatives.

Potential energy surfaces (PES) for rotation about the N-C(sp(3)) or N-C(aryl) bond and energies of stationary points on PES for rotation about the C(sp(2))-N bond are reported for methylurea, ethylurea, isopropylurea, tert-butylurea, and phenylurea, using the B3LYP/DZVP2 and MP2/aug-cc-pVDZ methods. The analysis of alkylureas reveals cis and (less stable) trans isomers that adopt anti geometries, whereas syn geometries do not correspond to stationary points. In contrast, the analysis of phenylurea reveals that the lowest energy form at the MP2 level is a trans isomer in a syn geometry. The fully optimized geometries are in good agreement with crystal structure data, and PESs are consistent with the experimental dihedral angle distribution. Rotation about the C(sp(2))-N bond in alkylureas and phenylurea is slightly more hindered (8.6-9.4 kcal/mol) than the analogous motion in the unsubstituted molecule (8.2 kcal/mol). At the MP2 level of theory, the maximum barriers to rotation for the methyl, ethyl, isopropyl, tert-butyl, and phenyl groups are predicted to be 0.9, 6.2, 6.0, 4.6, and 2.4 kcal/mol, respectively. The results are used to benchmark the performance of the MMFF94 force field. Systematic discrepancies between MMFF94 and MP2 results were improved by modification of several torsional parameters.

Alkylation↗

PubMed2006

Conformational preferences and internal rotation in alkyl- and phenyl-substituted thiourea derivatives.

Potential energy surfaces (PES) for rotation about the N-C(sp(3)) or N-C(aryl) bond and energies of stationary points on PES for rotation about the C(sp(2))-N bond are reported for methylthiourea, ethylthiourea, isopropylthiourea, tert-butylthiourea, and phenylurea, using the MP2/aug-cc-pVDZ method. Analysis of alkylthioureas shows that conformations, with alkyl groups cis to the sulfur atom, are more stable (by 0.4-1.5 kcal/mol) than the trans forms. All minima adopt anti configurations with respect to nitrogen pyramidalization, whereas syn configurations are not stationary points on the MP2 potential surface. In contrast, analysis of phenylthiourea reveals that a trans isomer in a syn geometry is the global minimum, whereas a cis isomer in an anti geometry is a local minimum with a relative energy of 2.7 kcal/mol. Rotation about the C(sp(2))-N bond in alkyl and phenyl thioureas is slightly more hindered (9.1-10.2 kcal/mol) than the analogous motion in the unsubstituted molecule (8.6 kcal/mol). The maximum barriers to rotation for the methyl, ethyl, isopropyl, tert-butyl, and phenyl substituents are predicted to be 1.2, 8.9, 8.6, 5.3, and 0.9 kcal/mol, respectively. Corresponding PESs are consistent with the experimental dihedral angle distribution observed in crystal structures. The results of the electronic structure calculations are used to benchmark the performance of the MMFF94 force field. Systematic discrepancies between MMFF94 and MP2 results were improved by modification of selected torsion parameters and one of the van der Waals parameters for sulfur.

Journal Article↗

PubMed2026

Towards efficient perturbation for the noncoding genome.

Deciphering the functionality of the noncoding genome, which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes, remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single-guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancers, dual-SpCas9 outperforms other methods with superior efficiency in destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model, DeepDC, with an associated web server, is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

CRISPR-Cas Systems↗

PubMed2002

Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression.

BACKGROUND/PURPOSE: There is a paucity of outcome prediction models for injured children. Using the National Pediatric Trauma Registry (NPTR), the authors developed an artificial neural network (ANN) to predict pediatric trauma death and compared it with logistic regression (LR). METHODS: Patients in the NPTR from 1996 through 1999 were included. Models were generated using LR and ANN. A data search engine was used to generate the ANN with the best fit for the data. Input variables included anatomic and physiologic characteristics. There was a single output variable: probability of death. Assessment of the models was for both discrimination (ROC area under the curve) and calibration (Lemeshow-Hosmer C-Statistic). RESULTS: There were 35,385 patients. The average age was 8.1 +/- 5.1 years, and there were 1,047 deaths (3.0%). Both modeling systems gave excellent discrimination (ROC A(z): LR = 0.964, ANN = 0.961). However, LR had only fair calibration, whereas the ANN model had excellent calibration (L/H C stat: LR = 36, ANN = 10.5). CONCLUSIONS: The authors were able to develop an ANN model for the prediction of pediatric trauma death, which yielded excellent discrimination and calibration exceeding that of logistic regression. This model can be used by trauma centers to benchmark their performance in treating the pediatric trauma population.

Calibration↗

PubMed2005

Worldwide perspective of the quality of care provided to hospitalized patients with community-acquired pneumonia: results from the CAPO international cohort study.

National organizations from multiple countries have developed evidence-based recommendations for the management of hospitalized patients with community-acquired pneumonia (CAP). Good quality of care in CAP can be defined as patient care provided in compliance with evidence-based recommendations. To evaluate the quality of care provided to hospitalized patients with CAP, an international network of investigators is collecting local data on quality indicators from 36 hospitals in 14 countries. Participating countries in four regions are performing worldwide benchmarking: North America (region I), Europe (region II), Latin America (region III), and Asia and Africa (region IV). The quality of care provided to 2750 hospitalized patients with CAP was evaluated in the following areas: diagnosis, hospitalization, respiratory isolation, microbiological workup, empirical therapy, switch therapy, hospital discharge, and prevention. The greatest opportunities for improvement were identified in the areas of prevention of CAP, initial empirical therapy, and switch from intravenous to oral antibiotics. This study indicates that the care recommended by national guidelines is not being appropriately delivered to adults in all regions of the world. New interventions to advance quality of care are necessary to improve clinical and economic outcomes in CAP.

Community-Acquired Infections↗

PubMed2004

Isolation and identification of Burkholderia cepacia by participants in an external Quality Assurance Program (QAP) between 1994 and 1999.

AIM: External quality assurance programs (QAPs) provide an opportunity to benchmark laboratory performance according to the profile of specimens received. Participant confidentiality is maintained within each group of laboratories whose performance is measured using similar, repetitive exercises. Isolation and identification of Burkholderia cepacia from simulated cystic fibrosis (CF) sputa was a clinically relevant exercise that provided a model for this analytical approach. METHODS: Between 1994 and 1999, six Royal College of Pathologists of Australasia (RCPA) Microbiology QAPs included four simulated CF sputa and two panels of oxidative Gram-negative bacilli. Laboratories were grouped according to experience with CF sputa disclosed by two questionnaires. Data were analysed by laboratory group for ability to isolate and identify B. cepacia. RESULTS: Three laboratory groups annually received >100 CF sputa (CF>100), 100 CF sputa or fewer, or did not regularly receive CF sputa. CF>100 laboratories inoculated more isolation media, were more likely to use selective media and were less likely to misidentify B. cepacia than the other groups. Improved performance by CF>100 laboratories was marked after the first exercise and remained at a high level compared with the other two groups. This trend in performance was also apparent for Pseudomonas aeruginosa although the numbers of errors were less than for B. cepacia. CONCLUSIONS: These exercises demonstrated consistently improved performance only among CF>100 laboratories. The future criteria for laboratory accreditation may include performance as well as participation in QAPs, placing additional burdens on organisers and participants.

Animals↗

PubMed2005

Correcting BLAST e-values for low-complexity segments.

The statistical estimates of BLAST and PSI-BLAST are of extreme importance to determine the biological relevance of sequence matches. While being very effective in evaluating most matches, these estimates usually overestimate the significance of matches in the presence of low complexity segments. In this paper, we present a model, based on divergence measures and statistics of the alignment structure, that corrects BLAST e-values for low complexity sequences without filtering or excluding them and generates scores that are more effective in distinguishing true similarities from chance similarities. We evaluate our method and compare it to other known methods using the Gene Ontology (GO) knowledge resource as a benchmark. Various performance measures, including ROC analysis, indicate that the new model improves upon the state of the art. The program is available at biozon.org/ftp/ and www.cs.technion.ac.il/ approximately itaish/lowcomp/.

Amino Acid Sequence↗

PubMed2006

Efficient calculation of interval scores for DNA copy number data analysis.

DNA amplifications and deletions characterize cancer genome and are often related to disease evolution. Microarray-based techniques for measuring these DNA copy-number changes use fluorescence ratios at arrayed DNA elements (BACs, cDNA, or oligonucleotides) to provide signals at high resolution, in terms of genomic locations. These data are then further analyzed to map aberrations and boundaries and identify biologically significant structures. We develop a statistical framework that enables the casting of several DNA copy number data analysis questions as optimization problems over real-valued vectors of signals. The simplest form of the optimization problem seeks to maximize phi(I) = Sigmanu(i)/radical|I| over all subintervals I in the input vector. We present and prove a linear time approximation scheme for this problem, namely, a process with time complexity O (nepsilon(-2)) that outputs an interval for which phi(I) is at least Opt/alpha(epsilon), where Opt is the actual optimum and alpha(epsilon) --> 1 as epsilon --> 0. We further develop practical implementations that improve the performance of the naive quadratic approach by orders of magnitude. We discuss properties of optimal intervals and how they apply to the algorithm performance. We benchmark our algorithms on synthetic as well as publicly available DNA copy number data. We demonstrate the use of these methods for identifying aberrations in single samples as well as common alterations in fixed sets and subsets of breast cancer samples.

Algorithms↗

PubMed2001

Model-based clustering and data transformations for gene expression data.

MOTIVATION: Clustering is a useful exploratory technique for the analysis of gene expression data. Many different heuristic clustering algorithms have been proposed in this context. Clustering algorithms based on probability models offer a principled alternative to heuristic algorithms. In particular, model-based clustering assumes that the data is generated by a finite mixture of underlying probability distributions such as multivariate normal distributions. The issues of selecting a 'good' clustering method and determining the 'correct' number of clusters are reduced to model selection problems in the probability framework. Gaussian mixture models have been shown to be a powerful tool for clustering in many applications. RESULTS: We benchmarked the performance of model-based clustering on several synthetic and real gene expression data sets for which external evaluation criteria were available. The model-based approach has superior performance on our synthetic data sets, consistently selecting the correct model and the number of clusters. On real expression data, the model-based approach produced clusters of quality comparable to a leading heuristic clustering algorithm, but with the key advantage of suggesting the number of clusters and an appropriate model. We also explored the validity of the Gaussian mixture assumption on different transformations of real data. We also assessed the degree to which these real gene expression data sets fit multivariate Gaussian distributions both before and after subjecting them to commonly used data transformations. Suitably chosen transformations seem to result in reasonable fits. AVAILABILITY: MCLUST is available at http://www.stat.washington.edu/fraley/mclust. The software for the diagonal model is under development. CONTACT: kayee@cs.washington.edu. SUPPLEMENTARY INFORMATION: http://www.cs.washington.edu/homes/kayee/model.

Algorithms↗

PubMed2026

Comprehensive Evaluation and Explainable Interpretation of Peptide-HLA Binding Prediction Tools.

Accurate prediction of peptide binding to human leukocyte antigen class I (HLA-I) molecules is critical for advancing immunological research, particularly in vaccine design and immunotherapy. However, limitations in model performance, interpretability, and dataset quality impede the widespread adoption of existing predictive tools. Here, we present a comprehensive evaluation of 17 HLA-I peptide binding prediction models, utilizing a meticulously curated dataset comprising over 290,000 peptides spanning 44 HLA-I alleles. We assessed model accuracy, robustness, and interpretability, employing explainability techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to elucidate underlying prediction mechanisms. Our results reveal substantial performance disparities, with self-attention-based models, including STMHCpan and BigMHC, exhibiting superior accuracy. Notably, the capsule network model CapsNet-MHC_AN demonstrated robust performance. Models trained on eluted ligand datasets outperformed those relying on binding affinity data, underscoring the critical role of high-quality training data. Ensemble and multi-algorithm approaches further improved prediction reliability. These findings highlight the need for ongoing innovation in model architecture, integration of diverse and high-quality datasets, and incorporation of structural predictors to develop more accurate, interpretable, and clinically applicable HLA-I peptide binding prediction tools.

HLA-I binding↗

PubMed2026

Genome-resolved assessment of archaeal diversity in full-scale anaerobic digesters reveals variability in mcrA primer coverage.

AIMS: Methanogenic archaea are key players in anaerobic digestion, driving methane production in biogas reactors. This study aimed to assess the diversity of methanogenic archaea in full-scale anaerobic digesters using genome-resolved metagenomics and to systematically evaluate the taxonomic coverage of commonly used mcrA-targeted qPCR primer sets against this genomic framework. METHODS AND RESULTS: Methanogenic diversity was assessed using 113 dereplicated archaeal metagenome-assembled genomes (MAGs) recovered from 109 full-scale anaerobic digesters treating diverse substrates. Genome-resolved analyses revealed a diverse archaeal community spanning multiple phyla, dominated by Halobacteriota and Methanobacteriota, with additional representatives from Methanobacteriota_B, Thermoplasmatota, and Thermoproteota. The presence of the mcrA gene was identified in a subset 55 MAGs, which were subsequently used as the genomic framework to evaluate six commonly used mcrA qPCR primer sets in silico. This subset clustered into nine phylogenetic groups and formed the basis for the primer coverage analysis. The evaluation revealed marked differences in taxonomic coverage among primer sets. Most primers preferentially detected Methanobacteriales and Methanosarcinales, while underrepresenting or excluding other methanogenic lineages, including H₂-dependent methylotrophic Methanomassiliicoccaceae. CONCLUSIONS: Commonly used mcrA primer sets differ substantially in their ability to capture methanogenic diversity, with some showing broad representation of reactor-associated methanogens and others exhibiting strong lineage-specific biases. Genome-resolved metagenomics provides an effective framework for benchmarking primer performance and supports the selection and improvement of molecular tools for more accurate monitoring of anaerobic digestion systems.

Archaea↗

PubMed2006

The effects of dialysis on 131I kinetics and dosimetry in thyroid cancer patients--a pharmacokinetic model.

Currently, an accepted post-surgical treatment of patients with thyroid carcinoma is administration of an ablative dose of I. This treatment is well established based on extensive experience and modeling. However, for patients with renal disease, reduced iodine removal rates result in controversial thyroid doses and potentially excessive red bone marrow doses. There are differences of opinion regarding I dose recommendations ranging from a reduction in dose to an increase in dose compared with conventional amounts. Determination of suitable doses must take into account varying dialysis protocols and absorbed dose considerations to the thyroid and sensitive tissues such as red bone marrow. The specific aim of this study was to develop a simple yet comprehensive compartmental model for I kinetics in patients with thyroid carcinoma and end stage renal disease, which accounts for dialysis and provides absorbed dose estimates for the thyroid as well as the red bone marrow. STELLA, a compartmental modeling software program, was used to develop a kinetic model that includes the blood pool, thyroid, gastrointestinal tract, kidneys, bladder, and a conventional dialysis machine. Benchmarking was performed to demonstrate the validity of the model with data obtained from ICRP 30 and MIRD Dose Estimate Report No. 5. Iodine kinetics were simulated for normal patients, thyroid cancer patients, and patients with thyroid cancer and renal failure undergoing two standard types of dialysis, hemodialysis and continuous ambulatory peritoneal dialysis (CAPD). Results in this work show that thyroid doses to patients with thyroid cancer and renal failure on hemodialysis or CAPD are slightly higher than doses to patients with thyroid cancer and normal renal function. These results further indicate that red bone marrow doses to patients with thyroid cancer and renal failure on dialysis can be significantly higher than red bone marrow doses to patients with thyroid cancer and normal renal function, and thus these patients could benefit from a reduction in administered activity. Thyroid doses and red bone marrow doses to patients on standard hemodialysis depend on both dialysis frequency and the time interval between administration and first dialysis. The results in this study provide guidelines on how much activity a patient on dialysis should receive based on thyroid and red bone marrow absorbed dose (Gy MBq) considerations. This study should help to clarify some of the contradictory recommendations regarding I dose for thyroid carcinoma patients with renal failure.

Bone Marrow↗

PubMed2005

Structural similarity to bridge sequence space: finding new families on the bridges.

Structures for protein domains have increased rapidly in recent years owing to advances in structural biology and structural genomics projects. New structures are often similar to those solved previously, and such similarities can give insights into function by linking poorly understood families to those that are better characterized. They also allow the possibility of combing information to find still more proteins adopting a similar structure and sometimes a similar function, and to reprioritize families in structural genomics pipelines. We explore this possibility here by preparing merged profiles for pairs of structurally similar, but not necessarily sequence-similar, domains within the SMART and Pfam database by way of the Structural Classification of Proteins (SCOP). We show that such profiles are often able to successfully identify further members of the same superfamily and thus can be used to increase the sensitivity of database searching methods like HMMer and PSI-BLAST. We perform detailed benchmarks using the SMART and Pfam databases with four complete genomes frequently used as annotation benchmarks. We quantify the associated increase in structural information in Swissprot and discuss examples illustrating the applicability of this approach to understand functional and evolutionary relationships between protein families.

Protein Conformation↗

PubMed2001

Governing the borderlands: decoding the power of aid.

This article examines aid practice, that is, the public-private contractual networks that link donor governments, UN agencies, military establishments, NGOs, private companies and others, as a relation of global liberal governance. In order to fulfil this function, such networks embody what could be called the 'securitisation' of international assistance. Based upon ideas of human security and ameliorating the effects of poverty and vulnerability reduction, aid is now seen as playing a direct security role. Rather than being concerned with relations between states, the primary aim of this security paradigm is to modulate and change the behaviour of populations within them. In doing so, it is able to exploit the opportunities afforded by privatisation. At the same time, however, aid as security is confronted by its own particular problem of 'governing at a distance'; how can calculations made by leading states be transformed into actions at the global edge when a multitude of private and non-government implementors now intervene? The article concludes by examining the contribution of risk analysis to solving this problem and, especially, the development of new contractual regimes based around technical standardisation, benchmarking and performance auditing. Through such technologies, metropolitan states are learning how to manage the public-private networks of aid practice and, as a result, to govern the borderlands in new ways.

Humans↗

PubMed2000

Tuning diversity in bagged ensembles.

In this paper, we investigate how the level of diversity amongst individual neural networks in a bagged ensemble can significantly influence overall ensemble generalization performance. We propose a new technique that tunes this diversity so that ensemble generalization performance is optimized and evaluate its performance on benchmark regression data-sets.

Algorithms↗

PubMed2026

Computed tomography-guided precision biopsy combined with metagenomic next-generation sequencing for etiological diagnosis in patients with blood culture-negative systemic infections.

ObjectiveTo evaluate the diagnostic efficacy of computed tomography-guided percutaneous biopsy combined with metagenomic next-generation sequencing in patients with blood culture-negative systemic infections and to assess the clinical impact of using this combined strategy for etiological confirmation and guidance of targeted antimicrobial therapy.MethodsThis single-center retrospective observational cohort study enrolled 78 patients who met the Sepsis-3 consensus criteria for suspected systemic infection and had negative conventional microbiological work-ups (at least two sets of blood cultures) between April 2022 and March 2025. All patients underwent computed tomography-guided biopsy of radiologically identified infectious foci, with specimens processed concurrently for conventional culture and metagenomic next-generation sequencing. Diagnostic performance was benchmarked against the final comprehensive clinical diagnosis, and the influence of metagenomic next-generation sequencing findings on antimicrobial therapy modification was analyzed. Sample size calculation, based on a prior study estimating an metagenomic next-generation sequencing detection rate of 85% (α = 0.05, β = 0.2), indicated a minimum of 68 cases; accordingly, 78 patients were enrolled.ResultsComputed tomography-guided biopsy was technically successful in all 78 patients (100%). The pathogen detection rate of metagenomic next-generation sequencing (91.0%, 71/78) was significantly higher than that of conventional culture (55.1%, 43/78; p < 0.001). Using the final clinical diagnosis as the reference standard, metagenomic next-generation sequencing achieved a sensitivity of 94.7% (95% confidence interval: 86.9-98.5), specificity of 100.0% (95% confidence interval: 29.2-100.0), positive predictive value of 100.0% (95% confidence interval: 94.9-100.0), and negative predictive value of 42.9% (95% confidence interval: 9.9-81.6). Among the 35 culture-negative specimens, metagenomic next-generation sequencing established a definitive microbiological diagnosis in 28 cases (80.0%) and detected polymicrobial infections in 11 cases (14.1% of the cohort). Antimicrobial therapy was rationally adjusted based on metagenomic next-generation sequencing results in 69.2% (54/78) of the patients.ConclusionsThe integration of computed tomography-guided precision biopsy with metagenomic next-generation sequencing offers a highly effective diagnostic approach for blood culture-negative systemic infections. This synergistic strategy improves etiological diagnosis by providing high-yield target specimens that enable comprehensive, unbiased pathogen screening, facilitates differentiation between infectious and non-infectious etiologies, and supplies critical evidence for guiding precision antimicrobial therapy. These findings highlight the growing role of interventional radiology in the contemporary framework of precision infectious disease management.

Humans↗

PubMed1998

Size characteristics of larger academic human environmental health programs in the United States.

We have performed a benchmark exercise evaluating larger academic programs in human environmental health sciences. These programs are located at schools of public health and at other institutions that have NIEHS Centers of Excellence. The largest programs were those in which there was both an NIEHS center and a public health graduate education program. This suggests that there is synergy between environmental health sciences research and involvement in public and community health.

Data Collection↗

PubMedDate not supplied

Quality of care for coronary heart disease in two countries.

Coronary heart disease is the leading cause of death in the United States and England, and each country devotes substantial resources to its prevention and treatment. We review recent strategies for improving quality of care for coronary heart disease in each country, including clinical guidelines; national standards; performance reports; benchmarking, feedback, and professional leadership; and market-oriented approaches. These strategies highlight the importance of information systems, organizational culture, and incentives to improve the quality of care in both the decentralized health care system of the United States and England's more centralized system.

Benchmarking↗