Experimental Validation Studies Research Articles

Three-dimensional shear-flexure interaction model for analysis of non-planar reinforced concrete walls

This paper presents theoretical formulation and results of experimental validation studies of a novel three-dimensional macroscopic nonlinear model that captures the interaction between axial-flexural and shear behavior in reinforced concrete (RC) walls. The original Shear-Flexure-Interaction Multiple-Vertical-Line-Element-Model (SFI-MVLEM) developed previously by the authors, which is essentially a two-dimensional line element for rectangular walls subjected to in-plane loading, is extended in this study to a three-dimensional model formulation (SFI-MVLEM-3D) by applying geometric transformation of the element degrees of freedom that convert it into a four-node element, as well as by incorporating linear elastic out-of-plane behavior based on Kirchhoff plate theory. The proposed three-dimensional, four-node model element is implemented in the official version of open-source analysis software OpenSees and validated against experimental data obtained for four well-instrumented U-shaped wall specimens tested under complex multidirectional loading protocols. Comprehensive and detailed comparisons were conducted between various experimentally-measured and analytically-predicted wall responses including the load-displacement behavior of the wall specimens in various loading directions, contributions of nonlinear flexural and shear deformations to wall displacements, vertical and horizontal profiles of flexural strains, and crack patterns. Results presented demonstrate that the proposed analytical model captures, with good accuracy, most of the experimentally-measured responses of non-rectangular walls subjected to multidirectional loading. Model predictions of the wall lateral load capacity and local responses (strains) are, although reasonable, relatively less accurate when the walls are subjected to diagonal loading conditions, due to the plane-sections-remain-plane assumption implemented in the proposed model formulation that disregards the experimentally-observed shear lag effect.

NeuroCirc: an integrative resource of circular RNA expression in the human brain.

CircRNAs are covalently closed RNA molecules that are particularly abundant in the brain. While circRNA expression data from the human brain is rapidly accumulating, integration of large-scale datasets remains challenging and time-consuming, and consequently an integrative view of circRNA expression in the human brain is currently lacking. NeuroCirc is a web-based resource that allows interactive exploration of multiple types of circRNA data from the human brain, including large-scale expression datasets, circQTL data and circRNA expression across neuronal differentiation and cellular maturation time-courses. NeuroCirc also allows users to upload their own circRNA expression data and explore it in the integrative platform, thereby supporting circRNA prioritization for experimental validation and functional studies. NeuroCirc is freely available at: https://voineagulab.github.io/NeuroCirc/. The source code and user documentation are available at: https://github.com/Voineagulab/NeuroCirc. Supplementary data are available at Bioinformatics online.

The electrocardiographic forward problem: A benchmark study

BackgroundElectrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high. ObjectiveTo test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors. MethodsWe used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling. ResultsWe studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane. ConclusionFirst, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.

An improved enzymatic pre-hydrolysis strategy for efficient bioconversion of industrial pulp and paper sludge waste to bioethanol using a semi-simultaneous saccharification and fermentation process

The current study investigated an integrated fermentation process for efficient conversion of pulp and paper sludge (PPS) material to bio-ethanol. To achieve maximum yield of reducing sugar from the pretreated PPS, most influencing parameters such as enzyme dosages, surfactant dosages on PPS loadings were optimized in a batch saccharification process using response surface methodology. The experimental validation studies under optimal conditions of 6% (w/w) solid loading condition, 0.16% (w/w) of surfactant concentration and 158 FPU/gm of enzyme loading resulted in a maximum reducing sugar yield of 45 ± 3.75% (w/w). Separate batch saccharification studies were conducted with 4% to 7% (w/w) solid loading to formulate best operating conditions for fed-batch saccharification with 13% (w/w), 18% (w/w) and 22% (w/w) high solid loading. The PPS solid loading of 18% (w/w) in fed-batch saccharification resulted in maximal release of glucose and xylose of 79.56 g/L and 8.65 g/L respectively at 60 h. Further improvement in the conversion of PPS to bioethanol was established through adoption of fed-batch semi-simultaneous saccharification and co-fermentation (S-SSCF) process. Fermentation using co-cultivation of two yeast species namely P. stipitis NCIM 3499 and Baker’s yeast resulted in maximum ethanol concentration of 42.34 g/L with 0.53 g/g yield from 18% (w/w) solid loading. Thus, the current study demonstrated the potential application of PPS waste as a feedstock material for ethanol production, which can be adopted by industries as an environment-friendly alternative to common solid waste management.

Honey: A remedy for depression. An investigation by experimental validation and molecular docking studies

Depression also known as clinical depression, is a mental disorder of public health concern that should be properly and immediately treated because a poor medical follow up can lead to suicide resulting to death or major body self-infliction of harm. Prediction of the phytochemical compounds in honey on neurotransmitters and enzymes involved in depression were investigated by in-silico studies. This research was done to create docking scores, predict pharmacodynamics of honey and to identify the potential oral drugs used for the treatment of depression Studies have shown that honey and related by products like propolisto be very useful in the treatment of depression. In this preclinical research, animal models was used to demonstrate the antidepressant effect of honey on female albino mice. These tests included tail suspension test and forced swim test, during which they were shared into four groups. From the results obtained honey showed a promising antidepressant activity together with a better synergism with imipramine.

Integrated Network and Gene Ontology Analysis Identifies Key Genes and Pathways for Coronary Artery Diseases.

Background:The prevalence of Coronary Artery Disease (CAD) in developing countries is on the rise, owing to rapidly changing lifestyle. Therefore, it is imperative that the underlying genetic and molecular mechanisms be understood to develop specific treatment strategies. Comprehensive disease network and Gene Ontology (GO) studies aid in prioritizing potential candidate genes for CAD and also give insights into gene function by establishing gene and disease pathway relationships.Methods:In the present study, CAD-associated genes were collated from different data sources and protein-protein interaction network was constructed using STRING. Highly interconnected network clusters were inferred and GO analysis was performed.Results:Interrelation between genes and pathways were analyzed on ClueGO and 38 candidates were identified from 1475 CAD-associated genes, which were significantly enriched in CAD-related pathways such as metabolism and regulation of lipid molecules, platelet activation, macrophage derived foam cell differentiation, and blood coagulation and fibrin clot formation.Discussion:Integrated network and ontology analysis enables biomarker prioritization for common complex diseases such as CAD. Experimental validation and future studies on the prioritized genes may reveal valuable insights into CAD development mechanism and targeted treatment strategies.

Identification of the miRNA-mRNA regulatory pathways and a miR-21-5p based nomogram model in clear cell renal cell carcinoma.

BackgroundThe purpose of this study was to determine the key microRNAs (miRNAs) and their regulatory networks in clear cell renal cell carcinoma (ccRCC).MethodsFive mRNA and three microRNA microarray datasets were downloaded from the Gene Expression Omnibus database and used to screen the differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed with Metascape. A miRNA-mRNA network was mapped with the Cytoscape tool. The results were validated with data from The Cancer Genome Atlas (TCGA) and qRT-PCR. A nomogram model based on independent prognostic key DEMs, stage and grade was constructed for further investigation.ResultsA total of 26 key DEMs and 307 DEGs were identified. Dysregulation of four key DEMs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) was identified to correlate with overall survival. The results were validated with TCGA data and qRT-PCR. The nomogram model showed high accuracy in predicting the prognosis of patients with ccRCC.ConclusionWe identified 26 DEMs that may play vital roles in the regulatory networks of ccRCC. Four miRNAs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) were considered as potential biomarkers in the prognosis of ccRCC, among which only miR-21-5p was found to be an independent prognostic factor. A nomogram model was then created on the basis of independent factors for better prediction of prognosis for patients with ccRCC. Our results suggest a need for further experimental validation studies.

A bioinformatic study of antimicrobial peptides identified in the Black Soldier Fly (BSF) Hermetia illucens (Diptera: Stratiomyidae)

Antimicrobial peptides (AMPs) play a key role in the innate immunity, the first line of defense against bacteria, fungi, and viruses. AMPs are small molecules, ranging from 10 to 100 amino acid residues produced by all living organisms. Because of their wide biodiversity, insects are among the richest and most innovative sources for AMPs. In particular, the insect Hermetia illucens (Diptera: Stratiomyidae) shows an extraordinary ability to live in hostile environments, as it feeds on decaying substrates, which are rich in microbial colonies, and is one of the most promising sources for AMPs. The larvae and the combined adult male and female H. illucens transcriptomes were examined, and all the sequences, putatively encoding AMPs, were analysed with different machine learning-algorithms, such as the Support Vector Machine, the Discriminant Analysis, the Artificial Neural Network, and the Random Forest available on the CAMP database, in order to predict their antimicrobial activity. Moreover, the iACP tool, the AVPpred, and the Antifp servers were used to predict the anticancer, the antiviral, and the antifungal activities, respectively. The related physicochemical properties were evaluated with the Antimicrobial Peptide Database Calculator and Predictor. These analyses allowed to identify 57 putatively active peptides suitable for subsequent experimental validation studies.

State-of-the-art models for mechanical performance of carbon-glass hybrid composites in wind turbine blades

Wind turbine blades are a key growth market for fibre-hybrid composites, as they offer the potential for higher turbine efficiency at a reduced cost. This paper therefore overviews the developments of KU Leuven models for mechanical properties relevant to wind turbine blades, including fibre-hybridisation, size scaling effects, flexure, transverse cracking, stress relaxation and fatigue. The strengths and limitations of the models will be highlighted with a particular focus on relevance for wind turbine blades. While significant steps forward have been made, the various developments still need to be incorporated in one all-encompassing model, and further work is needed on gathering the required input data and detailed experimental validation studies.

High-throughput virtual screening approach involving pharmacophore mapping, ADME filtering, molecular docking and MM-GBSA to identify new dual target inhibitors of PfDHODH and PfCytbc1 complex to combat drug resistant malaria

Emerging cases of drug resistance against Artemisinin combination therapies which are the current and the last line of defense against malaria makes the situation very alarming. Due to the liability of single-target drugs to be more prone to drug resistance, the trend of development of dual or multi-target inhibitors is emerging. Recently, a malaria box molecule, MMV007571 which is a well known new permeability pathways inhibitor was investigated to be also multi-targeting Plasmodium falciparum dihydroorotate dehydrogenase and cytochrome bc1 complex. The aspiration behind this study was to use the information of its pharmacophoric features essential for binding as two of its new targets. In this regard, high throughput virtual screening involving pharmacophore mapping, ADME filtering, molecular docking, and MM-GBSA calculations were carried out. This approach has lead to the identification of two new hits namely DT00V1902 and DT00V1922 which binds with -37.85 and -24.65 kcal/mol of more stable ΔG Bind energy at two targets than the lead molecule, MMV007571. The screened compounds are indicated to be carry improvement in binding potential and pharmacokinetic characters as per in silico studies. The authors propose that DT00V1902 and DT00V1922 can be forwarded for experimental validation and clinical studies for antimalarial chemotherapy. Communicated by Ramaswamy H. Sarma

A 16-plex Y-SNP typing system based on allele-specific PCR for the genotyping of Chinese Y-chromosomal haplogroups.

Y-chromosomal SNP (Y-SNP), with its stable inheritance and low mutation, can provide Supplementary information in forensic investigation. While commonly used Y-chromosomal STR haplotypes show their limitations, typing of Y-SNP would become a powerful complement. In this study, a 16-plex Y-SNP typing system based on allele-specific PCR (AS-PCR) was developed to discriminate four dominant Y-chromosomal haplogroups (C-M130, D-CTS3946, N-M231, and O-M175) and 12 predominant sub-haplogroups of O-M175 (O1a-M119, O1a1a1a-CTS3265, O1b-M268, O1b1a2-Page59, O2-M122, O2a1-L127.1, O2a1b-F240, O2a1b1a1-CTS5820, O2a2-P201, O2a2b1a1-M177, O2a2b1a1a1a-Y17728, O2a2b1a2-F114). A series of experimental validation studies including sensitivity, species specificity, male-female mixture and inhibition were performed. The discrimination of the typing system was preliminarily proved with a haplogroup diversity of 0.9239. Altogether, the Y-SNP typing system based on AS-PCR should be capable of distinguishing China's dominant Y-chromosomal haplogroups in a rapid and reliable manner, thus can be employed as a useful complement in forensic casework.

Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes.

PURPOSEThe availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data.METHODSHere, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes.RESULTSTo demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms.CONCLUSIONOur network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

A Novel Panel of 80 RNA Biomarkers with Differential Expression in Multiple Human Solid Tumors against Healthy Blood Samples.

The aim of this study was to identify genes with higher expression in solid tumor cells by comparing human tumor biopsies with healthy blood samples using both in silico statistical analysis and experimental validations. This approach resulted in a novel panel of 80 RNA biomarkers with high discrimination power to detect circulating tumor cells in blood samples. To identify the 80 RNA biomarkers, Affymetrix HG-U133 plus 2.0 microarrays datasets were used to compare breast tumor tissue biopsies and breast cancer cell lines with blood samples from patients with conditions other than cancer. A total of 859 samples were analyzed at the discovery stage, consisting of 417 mammary tumors, 41 breast lines, and 401 control samples. To confirm this discovery, external datasets of eight types of tumors were used, and experimental validation studies (NanoString n-counter gene expression assay) were performed, totaling 5028 samples analyzed. In these analyses, the 80 biomarkers showed higher expression in all solid tumors analyzed relative to healthy blood samples. Experimental validation studies using NanoString assay confirmed the results were not dependent of the gene expression platform. A panel of 80 RNA biomarkers was described here, with the potential to detect solid tumor cells present in the blood of multiple tumor types.

A sequential non-iterative approach for modeling multi-ionic species reactive transport during localized corrosion

Multi-ionic reactive transport modeling can provide a better understanding of localized corrosion in iron and steel alloys. However, one-step numerical methods used to solve reactive transport equations in a fully-coupled manner may suffer from poor conditioning and numerical convergences issues. In this paper, a sequential non-iterative approach (SNIA) is developed to enable robust numerical simulation of multi-ionic reactive transport models for localized corrosion. The electro-diffusive mass transport equations are decoupled from the homogeneous equilibrium chemical reactions equations, and are solved sequentially without iteration, leading to a two-step numerical method. The nonlinear mass transport equations are discretized using the standard finite element method, linearized and solved using the Newton-Raphson method. To ensure the non-negativity of ionic concentrations, the nonlinear chemical reactions equations are solved using the Newton-Raphson method together with the under-relaxation technique. Numerical studies, including model comparison, parametric and experimental validation studies, are conducted to simulate localized crevice/cavity corrosion of generic carbon steel and iron-chromium binary alloys and to demonstrate the efficacy of the proposed approach.

An investigation on citrus peel as the lignocellulosic feedstock for optimal reducing sugar synthesis with an additional scope for the production of hydrolytic enzymes from the aqueous extract waste

Abstract In the current study, the use citrus peels as the lignocellulosic feedstock for the production of reducing sugars and hydrolytic enzymes was evaluated. Citrus peels were subjected to a two-step pre-treatment procedure to obtain two major fractions-the water soluble pectin rich fraction and the water insoluble solid fraction enriched with cellulose and hemicellulose. Three parameters (solid loading, acid concentration and time) that were found to be critically influencing the acid hydrolysis process were optimized by using Response surface methodology (RSM) - central composite design (CCD) for the improved synthesis of reducing sugars. A maximal total reducing sugar concentration of 13.34 g/L was predicted for the optimal processing conditions of solid loading = 3.87% w/w, acid concentration = 1% w/w and time = 48.4 min and at the processing temperature of 121 °C in a steam autoclave. Experimental validation studies carried out at these optimal conditions showed a slightly higher reducing sugar concentration of 13.65 ± 0.3 g/L than the predicted value. Further studies demonstrating the use of pectin-rich liquid fraction as the nutrient medium for hydrolytic enzymes production resulted in enzyme activities of 5.38 ± 0.2 IU/mL for pectinase and 1.17 ± 0.1 FPU (Filter Paper Units) for cellulase, without supplementation of any salts to the medium. These results suggest that the pre-treated citrus peels can serve as a potent feedstock for bioethanol production, while by-products resulting from pre-treatment can be employed to produce hydrolytic enzymes such as pectinases and cellulases as demonstrated through this work.

Fluid‐structure interaction simulation of balance sensation in the avian lumbosacral organ

The lumbosacral organ (LSO) is an enlargement of the avian spinal cord hypothesized to play a role in balance sensation. Several lines of anatomical and neurophysiological evidence suggest that the LSO is directly integrated with spinal motor circuits, independent of the vestibular system. However, no model currently exists to explain its physiological function. We hypothesized that mechanosensors may respond to differential strain in spinal ligaments. We segmented iodine‐enhanced CT scans of the quail LSO to visualize the 3D anatomy of the structure. We then utilized two‐way fluid structure interaction simulations to model possible balance sensing function. Our modeling suggests that the LSO spinal ligaments are able to sense the directionality of an applied balance perturbation in the form of differential strain patterns. Furthermore, given anatomical constraints, it is unlikely that fluid velocity alone is sufficient to cause deformation in the spinal tissue. Inertia of the fluid and soft tissue itself play significant roles in generating the ligament strain. This work is the first simulation study of the LSO in any species and provides a methodological framework for further fluid dynamics modeling of the system. Our 3D reconstruction suggests potential anatomical constraints on fluid flow and supports the ligament strain hypothesis of balance sensing function. Higher resolution simulation work, as well as experimental validation studies in live animals are needed to more precisely evaluate the hypotheses for balance sensation in the LSO.Support or Funding InformationThis work was made possible by support from the American Physiological Society and the National Science Foundation Integrative Organismal Systems Award (Award No. IOS‐1238831).This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

U.S. Army Rotorcraft Research at Ames: New Frontier in Dynamics and Aeroelasticity

This paper presents an informal narrative of rotorcraft structural dynamics and aeroelasticity research initiated by the U.S. Army at NASA Ames Research Center in the early 1970s, when rotorcraft research became something of a new frontier in aerospace. The paper is focused on research conducted by Dewey Hodges and the authors, and it is based on a paper given as a tribute for the Dewey70 Session of the 2018 AIAA SciTech Forum. This rotorcraft research contributed a rigorous theoretical foundation that helped transform rotorcraft analysis from uncertainty into today’s sophisticated methodology that provides a sound basis for modern rotorcraft design. Dewey Hodges was arguably one of the principal architects of this transformation. Selected material is presented on the development of nonlinear beam theory for rotor blades, applications to rotorcraft aeroelastic stability, and experimental validation studies. The paper concludes by showing how these methods have been linked with today’s modern rotorcraft computational fluid dynamics.

Discovery of New Fusion Transcripts in a Cohort of Pediatric Solid Cancers at Relapse and Relevance for Personalized Medicine.

We hypothetized that pediatric cancers would more likely harbor fusion transcripts. To dissect the complexity of the fusions landscape in recurrent solid pediatric cancers, we conducted a study on 48 patients with different relapsing or resistant malignancies. By analyzing RNA sequencing data with a new in-house pipeline for fusions detection named ChimComp, followed by verification by real-time PCR, we identified and classified the most confident fusion transcripts (FTs) according to their potential biological function and druggability. The majority of FTs were predicted to affect key cancer pathways and described to be involved in oncogenesis. Contrary to previous descriptions, we found no significant correlation between the number of fusions and mutations, emphasizing the particularity to study pre-treated pediatric patients. A considerable proportion of FTs containing tumor suppressor genes was detected, reflecting their importance in pediatric cancers. FTs containing non-receptor tyrosine kinases occurred at low incidence and predominantly in brain tumors. Remarkably, more than 30% of patients presented a potentially druggable high-confidence fusion. In conclusion, we detected new oncogenic FTs in relapsing pediatric cancer patients by establishing a robust pipeline that can be applied to other malignancies, to detect and prioritize experimental validation studies leading to the development of new therapeutic options.

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

SummaryTranscriptome deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high-dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step toward linking tumor transcriptomic data with clinical outcomes. An R package, scripts, and data are available: https://github.com/wwylab/DeMixTallmaterials.

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17.

Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline that creates protein structure prediction using I-TASSER and infers functional insights for the target protein from the functional templates recognized by COFACTOR. As a case study, the pipeline was applied to all 66 PE1 proteins with unknown or insufficiently specific function (uPE1) on human chromosome 17 as of neXtProt 2017-07-01. Benchmark testing on a control set of 100 well-characterized proteins randomly selected from the same chromosome shows high Gene Ontology (GO) term prediction accuracies of 0.69, 0.57, and 0.67 for molecular function (MF), biological process (BP), and cellular component (CC), respectively. Three pipelines of function annotations (homology detection, protein-protein interaction network inference, and structure template identification) have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction made the major contribution to the enhancement of the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates. For the chromosome 17 uPE1 proteins, the I-TASSER/COFACTOR pipeline confidently assigned MF, BP, and CC for 13, 33, and 49 proteins, respectively, with predicted functions ranging from sphingosine N-acyltransferase activity and sugar transmembrane transporter to cytoskeleton constitution. We highlight the 13 proteins with confident MF predictions; 11 of these are among the 33 proteins with confident BP predictions and 12 are among the 49 proteins with confident CC. This study demonstrates a novel computational approach to systematically annotate protein function in the human proteome and provides useful insights to guide experimental design and follow-up validation studies of these uncharacterized proteins.