Stone decision engine accurately predicts stone removal and treatment complications for shock wave lithotripsy and laser ureterorenoscopy patients.

Kidney stones form when mineral salts crystallize in the urinary tract. While most stones exit the body in the urine stream, some can block the ureteropelvic junction or ureters, leading to severe lower back pain, blood in the urine, vomiting, and painful urination. Imaging technologies, such as X-rays or ureterorenoscopy (URS), are typically used to detect kidney stones. Subsequently, these stones are fragmented into smaller pieces using shock wave lithotripsy (SWL) or laser URS. Both treatments yield subtly different patient outcomes. To predict successful stone removal and complication outcomes, Artificial Neural Network models were trained on 15,126 SWL and 2,116 URS patient records. These records include patient metrics like Body Mass Index and age, as well as treatment outcomes obtained using various medical instruments and healthcare professionals. Due to the low number of outcome failures in the data (e.g., treatment complications), Nearest Neighbor and Synthetic Minority Oversampling Technique (SMOTE) models were implemented to improve prediction accuracies. To reduce noise in the predictions, ensemble modeling was employed. The average prediction accuracies based on Confusion Matrices for SWL stone removal and treatment complications were 84.8% and 95.0%, respectively, while those for URS were 89.0% and 92.2%, respectively. The average prediction accuracies for SWL based on Area-Under-the-Curve were 74.7% and 62.9%, respectively, while those for URS were 77.2% and 78.9%, respectively. Taken together, the approach yielded moderate to high accurate predictions, regardless of treatment or outcome. These models were incorporated into a Stone Decision Engine web application (http://peteranoble.com/webapps.html) that suggests the best interventions to healthcare providers based on individual patient metrics.

Unraveling the Enigma of Organismal Death: Insights, Implications, and Frontiers.

Significant knowledge gaps exist regarding the responses of cells, tissues, and organs to organismal death. Examining the survival mechanisms influenced by metabolism and environment, this research has the potential to transform regenerative medicine, redefine legal death, and provide insights into life's physiological limits, paralleling inquiries in embryogenesis.

Cryptic sequence features in the active postmortem transcriptome.

BACKGROUND: Our previous study found that more than 500 transcripts significantly increased in abundance in the zebrafish and mouse several hours to days postmortem relative to live controls. The current literature suggests that most mRNAs are post-transcriptionally regulated in stressful conditions. We rationalized that the postmortem transcripts must contain sequence features (3- to 9- mers) that are unique from those in the rest of the transcriptome and that these features putatively serve as binding sites for proteins and/or non-coding RNAs involved in post-transcriptional regulation. RESULTS: We identified 5117 and 2245 over-represented sequence features in the mouse and zebrafish, respectively, which represents less than 1.5% of all possible features. Some of these features were disproportionately distributed along the transcripts with high densities in the 3' untranslated regions of the zebrafish (0.3 mers/nt) and the open reading frames of the mouse (0.6 mers/nt). Yet, the highest density (2.3 mers/nt) occurred in the open reading frames of 11 mouse transcripts that lacked 3' or 5' untranslated regions. These results suggest the transcripts with high density of features might serve as 'molecular sponges' that sequester RNA binding proteins and/or microRNAs, and thus indirectly increase the stability and gene expression of other transcripts. In addition, some of the features were identified as binding sites for Rbfox and Hud proteins that are also involved in increasing transcript stability and gene expression. CONCLUSIONS: Our results are consistent with the hypothesis that transcripts involved in responding to extreme stress, such as organismal death, have sequence features that make them different from the rest of the transcriptome. Some of these features serve as putative binding sites for proteins and non-coding RNAs that determine transcript stability and fate. A small number of the transcripts have high density sequence features, which are presumably involved in sequestering RNA binding proteins and microRNAs and thus preventing regulatory interactions among other transcripts. Our results provide baseline data on post-transcriptional regulation in stressful conditions that has implications for regulation in disease, starvation, and cancer.

Amplicon Sequencing Reveals Microbiological Signatures in Spent Nuclear Fuel Storage Basins.

Water quality is an important determinant for the structural integrity of alloy cladded fuels and assemblies during long-term wet storage. Detailed characterization of a water filled storage basin for spent nuclear reactor fuel was performed following the formation and proliferation of an amorphous white flocculent. White precipitant was sampled throughout the storage basin for chemical and spectroscopic characterization, and environmental DNA was extracted for 454 pyrosequencing of bacterial 16S rRNA gene diversity. Accordingly, spectroscopic analyses indicated the precipitant to be primarily amorphous to crystalline aluminum (oxy) hydroxides with minor associated elemental components including Fe, Si, Ti, and U. High levels of organic carbon were co-localized with the precipitant relative to bulk dissolved organic concentrations. Bacterial densities were highly variable between sampling locations and with depth within the water filled storage basin; cell numbers ranged from 4 × 103to 4 × 104 cells/mL. Bacterial diversity that was physically associated with the aluminum (oxy) hydroxide complexes exceeded an estimated 4,000 OTUs/amplicon library (3% cutoff) and the majority of sequences were aligned to the families Burkholderiaceae (23%), Nitrospiraceae (23%), Hyphomicrobiaceae (17%), and Comamonadaceae (6%). We surmise that episodic changes in the physical and chemical properties of the basin contribute to the polymerization of aluminum (oxy) hydroxides, which in turn can chemisorb nutrients, carbon ligands and bacterial cells from the surrounding bulk aqueous phase. As such, these precipitants should establish favorable microhabitats for bacterial colonization and growth. Comparative analyses of 16S rRNA gene amplicon libraries across a selection of natural and engineered aquatic ecosystems were performed and microbial community and taxonomic signatures unique to the spent nuclear fuel (SNF) storage basin environment were revealed. These insights could spur the development of tractable bio-indicators that are specific of and diagnostic for water quality at discrete locations and finer scales of resolution, marking an important contribution for improved water quality and management of SNF storage facilities.

Gene Meter: Accurate abundance calculations of gene expression.

We previously reported that thousands of transcripts in the mouse and zebrafish significantly increased in abundance in a time series spanning from life to several days after death. Transcript abundances were determined by: calibrating each microarray probe using a dilution series of pooled RNAs, fitting the probe-responses to adsorption models, and back-calculating abundances using the probe signal intensity of a sample and the best fitting model. The accuracy of the abundance measurements was not assessed in our previous study because individual transcript concentrations in the calibration pool were not known. Accurate transcript abundances are highly desired for modeling the dynamics of biological systems and investigating how systems respond to perturbations. In this study, we show that accurate transcript abundances can be determined by calibrating the probes using a calibration pool of transcripts with known concentrations. Instructions for determining accurate transcript abundances using the Gene Meter approach are provided.

Gene expression in the twilight of death: The increase of thousands of transcripts has implications to transplantation, cancer, and forensic research.

After a vertebrate dies, many of its organ systems, tissues, and cells remain functional while its body no longer works as a whole. We define this state as the "twilight of death" - the transition from a living body to a decomposed corpse. We claim that the study of the twilight of death is important to ethical, legal and medical science. We examined gene expression at the twilight of death in the zebrafish and mouse reaching the conclusion that apparently thousands of transcripts significantly increase in abundance from life to several hours/days postmortem relative to live controls. Transcript dynamics of different genes provided "proof-of-principle" that models accurately predict an individual's elapsed-time-of-death (i.e. postmortem interval). While many transcripts were associated with survival and stress compensation, others were associated with epigenetic factors, developmental control, and cancer. Future studies are needed to determine whether the high incidence of cancer in transplant recipients is due to the postmortem processes in donor organs.

Accurate predictions of postmortem interval using linear regression analyses of gene meter expression data.

In criminal and civil investigations, postmortem interval is used as evidence to help sort out circumstances at the time of human death. Many biological, chemical, and physical indicators can be used to determine the postmortem interval - but most are not accurate. Here, we sought to validate an experimental design to accurately predict the time of death by analyzing the expression of hundreds of upregulated genes in two model organisms, the zebrafish and mouse. In a previous study, the death of healthy adults was conducted under strictly controlled conditions to minimize the effects of confounding factors such as lifestyle and temperature. A total of 74,179 microarray probes were calibrated using the Gene Meter approach and the transcriptional profiles of 1063 genes that significantly increased in abundance were assembled into a time series spanning from life to 48 or 96h postmortem. In this study, the experimental design involved splitting the transcription profiles into training and testing datasets, randomly selecting groups of profiles, determining the modeling parameters of the genes to postmortem time using over- and/or perfectly-defined linear regression analyses, and calculating the fit (R2) and slope of predicted versus actual postmortem times. This design was repeated several thousand to million times to find the top predictive groups of gene transcription profiles. A group of eleven zebrafish genes yielded R2 of 1 and a slope of 0.99, while a group of seven mouse liver genes yielded a R2 of 0.98 and a slope of 0.97, and seven mouse brain genes yielded a R2 of 0.95 and a slope of 0.87. In all cases, groups of gene transcripts yielded better postmortem time predictions than individual gene transcripts. The significance of this study is two-fold: selected groups of gene transcripts provide accurate prediction of postmortem time, and the successfully validated experimental design can now be used to accurately predict postmortem time in cadavers.

Tracing the dynamics of gene transcripts after organismal death.

In life, genetic and epigenetic networks precisely coordinate the expression of genes-but in death, it is not known if gene expression diminishes gradually or abruptly stops or if specific genes and pathways are involved. We studied this by identifying mRNA transcripts that apparently increase in relative abundance after death, assessing their functions, and comparing their abundance profiles through postmortem time in two species, mouse and zebrafish. We found mRNA transcript profiles of 1063 genes became significantly more abundant after death of healthy adult animals in a time series spanning up to 96 h postmortem. Ordination plots revealed non-random patterns in the profiles by time. While most of these transcript levels increased within 0.5 h postmortem, some increased only at 24 and 48 h postmortem. Functional characterization of the most abundant transcripts revealed the following categories: stress, immunity, inflammation, apoptosis, transport, development, epigenetic regulation and cancer. The data suggest a step-wise shutdown occurs in organismal death that is manifested by the apparent increase of certain transcripts with various abundance maxima and durations.

Datasets used to discover the microbial signatures of oral dysbiosis, periodontitis and edentulism in humans.

This article provides supporting data for the research article 'Microbial Signatures of Oral Dysbiosis, Periodontitis and Edentulism Revealed by Gene Meter Methodology' (M.C. Hunter, A.E. Pozhitkov, P.A. Noble, 2016) [1]. In that article, we determined the microbial abundance signatures for patient with periodontics, edentulism, or health using Gene Meter Technology. Here we provide the data used to make the DNA microarray and the resulting microbial abundance data that was determined using the calibrated probes and the 16S rRNA genes harvested from patients. The first data matrix contains two columns: one is the GenInfo Identifier (GI) numbers of the 16S rRNA gene sequences and the other is the corresponding oral bacterial taxonomy. The probes were then screened for redundancy and if they were found to be unique, they were synthesized onto the surface of the DNA microarrays. The second data matrix consists of the abundances of the 576 16S rRNA genes that was determined using the median value of all individual calibrated probes targeting each gene. The data matrix consists of 16 columns and 576 rows, with the columns representing the 16 patients and the rows representing 576 different oral microorganisms. The third data matrix consists of the abundances of 567 16S rRNA genes determined using the calibrated abundance of all aggregated probes targeting the same 16S rRNA gene. The data matrix of the aggregated probes consists of 16 samples and 567 rows.

Microbial signatures of oral dysbiosis, periodontitis and edentulism revealed by Gene Meter methodology.

Conceptual models suggest that certain microorganisms (e.g., the "red" complex) are indicative of a specific disease state (e.g., periodontitis); however, recent studies have questioned the validity of these models. Here, the abundances of 500+ microbial species were determined in 16 patients with clinical signs of one of the following oral conditions: periodontitis, established caries, edentulism, and oral health. Our goal was to determine if the abundances of certain microorganisms reflect dysbiosis or a specific clinical condition that could be used as a 'signature' for dental research. Microbial abundances were determined by the analysis of 138,718 calibrated probes using Gene Meter methodology. Each 16S rRNA gene was targeted by an average of 194 unique probes (n=25nt). The calibration involved diluting pooled gene target samples, hybridizing each dilution to a DNA microarray, and fitting the probe intensities to adsorption models. The fit of the model to the experimental data was used to assess individual and aggregate probe behavior; good fits (R2>0.90) were retained for back-calculating microbial abundances from patient samples. The abundance of a gene was determined from the median of all calibrated individual probes or from the calibrated abundance of all aggregated probes. With the exception of genes with low abundances (<2 arbitrary units), the abundances determined by the different calibrations were highly correlated (r~1.0). Seventeen genera were classified as 'signatures of dysbiosis' because they had significantly higher abundances in patients with periodontitis and edentulism when contrasted with health. Similarly, 13 genera were classified as 'signatures of periodontitis', and 14 genera were classified as 'signatures of edentulism'. The signatures could be used, individually or in combination, to assess the clinical status of a patient (e.g., evaluating treatments such as antibiotic therapies). Comparisons of the same patient samples revealed high false negatives (45%) for next-generation-sequencing results and low false positives (7%) for Gene Meter results.

A survey of biofilms on wastewater aeration diffusers suggests bacterial community composition and function vary by substrate type and time.

Aeration diffusers in wastewater treatment plants generate air bubbles that promote mixing, distribution of dissolved oxygen, and microbial processing of dissolved and suspended matter in bulk solution. Biofouling of diffusers represents a significant problem to wastewater treatment plants because biofilms decrease oxygen transfer efficiency and increase backpressure on the blower. To better understand biofouling, we conducted a pilot study to survey the bacterial community composition and function of biofilms on different diffuser substrates and compare them to those in the bulk solution. DNA was extracted from the surface of ethylene-propylene-diene monomer (EPDM), polyurethane, and silicone diffusers operated for 15 months in a municipal treatment plant and sampled at 3 and 9 months. The bacterial community composition and function of the biofilms and bulk solution were determined by amplifying the 16S rRNA genes and pyrosequencing the amplicons and raw metagenomic DNA. The ordination plots and dendrograms of the 16S rRNA and functional genes showed that while the bacterial community composition and function of the bulk solution was independent of sampling time, the composition and function of the biofilms differed by diffuser type and testing time. For the EPDM and silicone diffusers, the biofilm communities were more similar in composition to the bulk solution at 3 months than 9 months. In contrast, the bacteria on the polyurethane diffusers were more dissimilar to the bulk solution at 3 months than 9 months. Taken together, the survey showed that the community composition and function of bacterial biofilms depend on the diffuser substrate and testing time, which warrants further elucidation.

Discovery of Bioactive Metabolites in Biofuel Microalgae That Offer Protection against Predatory Bacteria.

Microalgae could become an important resource for addressing increasing global demand for food, energy, and commodities while helping to reduce atmospheric greenhouse gasses. Even though Chlorophytes are generally regarded safe for human consumption, there is still much we do not understand about the metabolic and biochemical potential of microscopic algae. The aim of this study was to evaluate biofuel candidate strains of Chlorella and Scenedesmus for the potential to produce bioactive metabolites when grown under nutrient depletion regimes intended to stimulate production of triacylglycerides. Strain specific combinations of macro- and micro-nutrient restricted growth media did stimulate neutral lipid accumulation by microalgal cultures. However, cultures that were restricted for iron consistently and reliably tested positive for cytotoxicity by in vivo bioassays. The addition of iron back to these cultures resulted in the disappearance of the bioactive components by LC/MS fingerprinting and loss of cytotoxicity by in vivo bioassay. Incomplete NMR characterization of the most abundant cytotoxic fractions suggested that small molecular weight peptides and glycosides could be responsible for Chlorella cytotoxicity. Experiments were conducted to determine if the bioactive metabolites induced by Fe-limitation in Chlorella sp. cultures would elicit protection against Vampirovibrio chlorellavorus, an obligate predator of Chlorella. Introduction of V. chlorellavorus resulted in a 72% decrease in algal biomass in the experimental controls after 7 days. Conversely, only slight losses of algal biomass were measured for the iron limited Chlorella cultures (0-9%). This study demonstrates a causal linkage between iron bioavailability and bioactive metabolite production in strains of Chlorella and Scenedesmus. Further study of this phenomenon could contribute to the development of new strategies to extend algal production cycles in open, outdoor systems while ensuring the protection of biomass from predatory losses.

Towards microbiome transplant as a therapy for periodontitis: an exploratory study of periodontitis microbial signature contrasted by oral health, caries and edentulism.

BACKGROUND: Conventional periodontal therapy aims at controlling supra- and subgingival biofilms. Although periodontal therapy was shown to improve periodontal health, it does not completely arrest the disease. Almost all subjects compliant with periodontal maintenance continue to experience progressive clinical attachment loss and a fraction of them loses teeth. An oral microbial transplant may be a new alternative for treating periodontitis (inspired by fecal transplant). First, it must be established that microbiomes of oral health and periodontitis are distinct. In that case, the health-associated microbiome could be introduced into the oral cavity of periodontitis patients. This relates to the goals of our study: (i) to assess if microbial communities of the entire oral cavity of subjects with periodontitis were different from or oral health contrasted by microbiotas of caries and edentulism patients; (ii) to test in vitro if safe concentration of sodium hypochlorite could be used for initial eradication of the original oral microbiota followed by a safe neutralization of the hypochlorite prior transplantation. METHODS: Sixteen systemically healthy white adults with clinical signs of one of the following oral conditions were enrolled: periodontitis, established caries, edentulism, and oral health. Oral biofilm samples were collected from sub- and supra-gingival sites, and oral mucosae. DNA was extracted and 16S rRNA genes were amplified. Amplicons from the same patient were pooled, sequenced and quantified. Volunteer's oral plaque was treated with saline, 16 mM NaOCl and NaOCl neutralized by ascorbate buffer followed by plating on blood agar. RESULTS: Ordination plots of rRNA gene abundances revealed distinct groupings for the oral microbiomes of subjects with periodontitis, edentulism, or oral health. The oral microbiome in subjects with periodontitis showed the greatest diversity harboring 29 bacterial species at significantly higher abundance compared to subjects with the other assessed conditions. Healthy subjects had significantly higher abundance in 10 microbial species compared to the other conditions. NaOCl showed strong antimicrobial properties; nontoxic ascorbate was capable of neutralizing the hypochlorite. CONCLUSIONS: Distinct oral microbial signatures were found in subjects with periodontitis, edentulism, or oral health. This finding opens up a potential for a new therapy, whereby a health-related entire oral microbial community would be transplanted to the diseased patient.

Interruption of Electrical Conductivity of Titanium Dental Implants Suggests a Path Towards Elimination Of Corrosion.

Peri-implantitis is an inflammatory disease that results in the destruction of soft tissue and bone around the implant. Titanium implant corrosion has been attributed to the implant failure and cytotoxic effects to the alveolar bone. We have documented the extent of titanium release into surrounding plaque in patients with and without peri-implantitis. An in vitro model was designed to represent the actual environment of an implant in a patient's mouth. The model uses actual oral microbiota from a volunteer, allows monitoring electrochemical processes generated by biofilms growing on implants and permits control of biocorrosion electrical current. As determined by next generation DNA sequencing, microbial compositions in experiments with the in vitro model were comparable with the compositions found in patients with implants. It was determined that the electrical conductivity of titanium implants was the key factor responsible for the biocorrosion process. The interruption of the biocorrosion current resulted in a 4-5 fold reduction of corrosion. We propose a new design of dental implant that combines titanium in zero oxidation state for osseointegration and strength, interlaid with a nonconductive ceramic. In addition, we propose electrotherapy for manipulation of microbial biofilms and to induce bone healing in peri-implantitis patients.

Distinctive thanatomicrobiome signatures found in the blood and internal organs of humans.

According to the Human Microbiome Project, 90% of the cells in a healthy adult body are microorganisms. What happens to these cells after human host death, defined here as the thanatomicrobiome (i.e., thanatos-, Greek defn., death), is not clear. To fill the void, we examined the thanatomicrobiome of the spleen, liver, brain, heart and blood of human cadavers. These organs are thought to be devoid of microorganisms in a healthy adult host. We report that the thanatomicrobiome was highly similar among organ tissues from the same cadaver but very different among the cadavers possibly due to differences in the elapsed time since death and/or environmental factors. Isolation of microbial DNA from cadavers is known to be a challenge. We compared the effectiveness of two methods by amplifying the 16S rRNA genes and sequencing the amplicons from four cadavers. Paired comparisons revealed that the conventional DNA extraction method (bead-beating in phenol/chloroform/bead-beating followed by ethanol precipitation) yielded more 16S rRNA amplicons (28 of 30 amplicons) than a second method (repeated cycles of heating/cooling followed by centrifugation to remove cellular debris) (19 of 30 amplicons). Shannon diversity index of the 16S rRNA genes revealed no significant difference by extraction method. The present report provides a proof of principle that the thanatomicrobiome may be an efficient biomarker to study postmortem transformations of cadavers.

A revised design for microarray experiments to account for experimental noise and uncertainty of probe response.

BACKGROUND: Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. RESULTS: Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. CONCLUSION: The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.

Physico-chemical foundations underpinning microarray and next-generation sequencing experiments.

Hybridization of nucleic acids on solid surfaces is a key process involved in high-throughput technologies such as microarrays and, in some cases, next-generation sequencing (NGS). A physical understanding of the hybridization process helps to determine the accuracy of these technologies. The goal of a widespread research program is to develop reliable transformations between the raw signals reported by the technologies and individual molecular concentrations from an ensemble of nucleic acids. This research has inputs from many areas, from bioinformatics and biostatistics, to theoretical and experimental biochemistry and biophysics, to computer simulations. A group of leading researchers met in Ploen Germany in 2011 to discuss present knowledge and limitations of our physico-chemical understanding of high-throughput nucleic acid technologies. This meeting inspired us to write this summary, which provides an overview of the state-of-the-art approaches based on physico-chemical foundation to modeling of the nucleic acids hybridization process on solid surfaces. In addition, practical application of current knowledge is emphasized.

Beyond Affymetrix arrays: expanding the set of known hybridization isotherms and observing pre-wash signal intensities.

Microarray hybridization studies have attributed the nonlinearity of hybridization isotherms to probe saturation and post-hybridization washing. Both processes are thought to distort 'true' target abundance because immobilized probes are saturated with excess target and stringent washing removes loosely bound targets. Yet the paucity of studies aimed at understanding hybridization and dissociation makes it difficult to align physicochemical theory to microarray results. To fill the void, we first examined hybridization isotherms generated on different microarray platforms using a ribosomal RNA target and then investigated hybridization signals at equilibrium and after stringent wash. Hybridization signal at equilibrium was achieved by treating the microarray with isopropanol, which prevents nucleic acids from dissolving into solution. Our results suggest that (i) the shape of hybridization isotherms varied by microarray platform with some being hyperbolic or linear, and others following a power-law; (ii) at equilibrium, fluorescent signal of different probes hybridized to the same target were not similar even with excess of target and (iii) the amount of target removed by stringent washing depended upon the hybridization time, the probe sequence and the presence/absence of nonspecific targets. Possible physicochemical interpretations of the results and future studies are discussed.

Use of hidden correlations in short oligonucleotide array data are insufficient for accurate quantification of nucleic acid targets in complex target mixtures.

Nonspecific target binding (i.e., cross-hybridization) is a major challenge for interpreting oligonucleotide microarray results because it is difficult to determine what portion of the signal is due to binding of complementary (specific) targets to a probe versus that due to binding of nonspecific targets. Solving this challenge would be a major accomplishment in microarray research potentially allowing quantification of targets in biological samples. Marcelino et al. recently described a new approach that reportedly solves this challenge by iteratively deconvoluting 'true' specific signal from raw signal, and quantifying ribosomal (rRNA) sequences in artificial and natural communities (i.e., "Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data", Proc. Natl. Acad. Sci. 103, 13629-13634). We evaluated their approach using high-density oligonucleotide microarrays and Latin-square designed experiments consisting of 6 and 8 rRNA targets in 16 different artificial mixtures. Our results show that contrary to the claims in the article, the hidden correlations in the microarray data are insufficient for accurate quantification of nucleic acid targets in complex artificial target mixtures.